Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework.

PubMed

Ning, Kaida; Chen, Bo; Sun, Fengzhu; Hobel, Zachary; Zhao, Lu; Matloff, Will; Toga, Arthur W

2018-08-01

A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them. Copyright © 2018 Elsevier Inc. All rights reserved.

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

Bernstein, A. M., E-mail: bernstein@mit.edu

Small angle electron scattering with intense electron beams opens up the possibility of performing almost real photon induced reactions with thin, polarized hydrogen and few body targets, allowing for the detection of low energy charged particles. This promises to be much more effective than conventional photon tagging techniques. For photo-pion reactions some fundamental new possibilities include: tests of charge symmetry in the N-N system by measurement of the neutron-neutron scattering length a{sub nn} in the and ggrD → π{sup +}nn reaction; tests of isospin breaking due to the mass difference of the up and down quarks; measurements with polarized targetsmore » are sensitive to πN phase shifts and will test the validity of the Fermi-Watson (final state interaction) theorem. All of these experiments will test the accuracy and energy region of validity of chiral effective theories.« less

Determination of protein-dye association by near infrared fluorescence-detected circular dichroism.

PubMed

Meadows, F; Narayanan, N; Patonay, G

2000-01-10

Near-infrared (NIR) squarylium dye spectral properties were evaluated by absorption, fluorescence, circular dichroism (CD), and fluorescence-detected circular dichroism (FDCD). Substituents of the two NN dyes differed at R(1) and R(2), located symmetrically on the chromophore. The side chains of NN525 are R(1)=hexanoic acid, R(2)=butyl sulfonate and R(1)=R(2)=ethyl for NN127. FDCD signals were first confirmed by denaturing BSA with 2-8 M urea showing a diminution of dye FDCD peaks, but no change occurred in spectral properties of the dyes in urea. This indicated that the observed cotton effects occurred by noncovalent interactions with the secondary structure of the protein. The average BSA-dye association constants found by fluorescence, absorbance, and FDCD were 1.27 x 10(6) (n=1) and 3.3 x 10(6) M(-1) (n=1) for NN127 and NN525 respectively. These values were in good agreement when calculated by the three spectroscopic methods validating the use of NIRFDCD for optical parameter calculations. These results are useful to describe NIR squarylium dye labeling of BSA.

ΛΛ correlation function in Au + Au collisions at √ sNN = 200 GeV

DOE PAGES

Adamczyk, L.

2015-01-12

In this study, we present ΛΛ correlation measurements in heavy-ion collisions for Au+Au collisions at √ sNN = 200 GeV using the STAR experiment at the Relativistic Heavy-Ion Collider (RHIC). The Lednický-Lyuboshitz analytical model has been used to fit the data to obtain a source size, a scattering length and an effective range. Implications of the measurement of the ΛΛ correlation function and interaction parameters for di-hyperon searches are discussed.

Study of the Hyperon-Nucleon Interaction in Exclusive Λ Photoproduction off the Deuteron

NASA Astrophysics Data System (ADS)

Zachariou, Nicholas; CLAS Collaboration

2014-09-01

Understanding the nature of the nuclear force in terms of the fundamental degrees of freedom of the theory of strong interaction, Quantum Chromodynamics (QCD), is one of the primary goals of modern nuclear physics. While the nucleon-nucleon (NN) interaction has been studied for decades, a systematic description of the NN potential has been achieved only recently with the development of low-energy Effective Field Theories (EFT). To obtain a comprehensive understanding of the strong interaction, dynamics involving strange baryons must be studied. Currently, little is known about the properties of the hyperon-nucleon (YN) and the hyperon-hyperon (YY) interactions. In this talk I will describe our current research of the Λn interaction using the E06-103 experiment performed with the CLAS detector in Hall B at Jefferson Lab. The large kinematic coverage of the CLAS combined with the exceptionally high quality of the experimental data allows to identify and select final-state interaction events in the reaction γd -->K+ Λn and to establish their kinematical dependencies. The large set of observables we aim to obtain will provide tight constraints on modern YN potentials. I will present the current status of the project and will discuss future incentives. Understanding the nature of the nuclear force in terms of the fundamental degrees of freedom of the theory of strong interaction, Quantum Chromodynamics (QCD), is one of the primary goals of modern nuclear physics. While the nucleon-nucleon (NN) interaction has been studied for decades, a systematic description of the NN potential has been achieved only recently with the development of low-energy Effective Field Theories (EFT). To obtain a comprehensive understanding of the strong interaction, dynamics involving strange baryons must be studied. Currently, little is known about the properties of the hyperon-nucleon (YN) and the hyperon-hyperon (YY) interactions. In this talk I will describe our current research of the Λn interaction using the E06-103 experiment performed with the CLAS detector in Hall B at Jefferson Lab. The large kinematic coverage of the CLAS combined with the exceptionally high quality of the experimental data allows to identify and select final-state interaction events in the reaction γd -->K+ Λn and to establish their kinematical dependencies. The large set of observables we aim to obtain will provide tight constraints on modern YN potentials. I will present the current status of the project and will discuss future incentives. for the CLAS Collaboration.

Effects of dietary aluminum, calcium, and phosphorus on egg and bone of European starlings

USGS Publications Warehouse

Miles, A.K.; Grue, C.E.; Pendleton, G.W.; Soares, J.H.

1993-01-01

Egg and bone of passerine birds nesting in acidified habitats may be affected by high levels of Al or P, or low levels of Ca. Nine treatments of three levels of dietary Al (target levels of 200, 1,000, and 5,000 ?g/g) and three levels of Ca:P (target levels of NN = 1.3% Ca: 0.9% P; LL = 0.19 Ca:0.45 P; LH = 0.19 Ca:1.65 P) were fed to 16-17 starling pairs during two breeding seasons. Eggs of starlings fed the LH diet were smaller and weighed less than eggs from the NN and LL treatments. Treatment effects on thickness, strength, and weight of eggshells were not consistent between seasons, probably because of differences in actual dietary levels of AI, Ca, and P or in incubation intervals. In one season, birds fed the highest Al diet had thicker eggshells than those from the other Al treatments (no effect from Ca:P); the following season, eggshells from the NN and LH treatments were thicker and stronger than those from the LL treatment. Eggshells from the NN treatment weighed more than those from the other Ca:P treatments. Starlings on the LH diet had the strongest femurs, but the effect was interactive with different levels of dietary AI. Effects of Ca:P on egg and bone were more evident than Al effects.

Seating Arrangement, Group Composition and Competition-driven Interaction: Effects on Students' Performance in Physics

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

Roxas, R. M.; Monterola, C.; Carreon-Monterola, S. L.

2010-07-28

We probe the effect of seating arrangement, group composition and group-based competition on students' performance in Physics using a teaching technique adopted from Mazur's peer instruction method. Ninety eight lectures, involving 2339 students, were conducted across nine learning institutions from February 2006 to June 2009. All the lectures were interspersed with student interaction opportunities (SIO), in which students work in groups to discuss and answer concept tests. Two individual assessments were administered before and after the SIO. The ratio of the post-assessment score to the pre-assessment score and the Hake factor were calculated to establish the improvement in student performance.more » Using actual assessment results and neural network (NN) modeling, an optimal seating arrangement for a class was determined based on student seating location. The NN model also provided a quantifiable method for sectioning students. Lastly, the study revealed that competition-driven interactions increase within-group cooperation and lead to higher improvement on the students' performance.« less

ESR study of molecular orientation and dynamics of nitronyl nitroxide radicals in CLPOT 1D nanochannels.

PubMed

Kobayashi, Hirokazu; Morinaga, Yuka; Fujimori, Etsuko; Asaji, Tetsuo

2014-07-10

New inclusion compounds (ICs) were prepared using the organic 1D nanochannels of 2,4,6-tris(4-chlorophenoxy)-1,3,5-triazine (CLPOT) as a nanosized template and nitronyl nitroxide (NN) radicals such as phenylnitronylnitroxide (PhNN) and p-nitrophenylnitronylnitroxide (p-NPNN). ESR measurements below 255 K for the CLPOT ICs diluted with spacer molecules gave rigid limit spectra similar to that for PhNN molecules in a glassy ethanol matrix at low temperature, which suggests isolation of the radical molecules. ESR measurements for them in the range of 290-400 K gave a modulated quintet ESR signal, which suggested uniaxial rotational diffusion of NN radicals in the nanochannels approximately around the principal y-axis of the g-tensors. In the ESR measurements to 430 K for the [(CLPOT)2-(p-NPNN)0.07] IC without spacers, the broader line width than the case in dilution was observed by inter-radical dipolar interaction. In every case, the rotational diffusion activation energies of NN radicals in the CLPOT nanochannels were several times larger than those of 2,2,6,6-tetramethyl-1-piperidinyloxyl (TEMPO) radical derivatives (4-X-TEMPO) in CLPOT nanochannels. This is expected due to the larger molecular size of NN radicals than 4-X-TEMPO or stronger interaction between NN radicals and the surrounding host or guest molecules.

Superexchange interaction in AIIBVI-based semimagnetic semiconductors. (in English)

NASA Astrophysics Data System (ADS)

Melnychuk, S. V.; Mykhaylevsky, Y. M.; Savchhuk, A. I.; Tryfonenko, D. M.

In this report Mn^{2+}, Fe^{2+} and Co^{2+} ions in the ground state in the presence of crystalline field T_d symmetry are considered. Superexchange interaction is performed via S, Se, Te ions. Theoretical calculations of the superexchange interaction integral J_{NN} have been carried out within the framework of Racah technique. Experimental values of J_{NN} for Cd_{1-x}Fe_{x}Te were obtained from the measurement of Faraday rotation temperature dependence.

Symmetric and asymmetric squarylium dyes as noncovalent protein labels: a study by fluorimetry and capillary electrophoresis.

PubMed

Welder, Frank; Paul, Beverly; Nakazumi, Hiroyuki; Yagi, Shigeyuki; Colyer, Christa L

2003-08-05

Noncovalent interactions between two squarylium dyes and various model proteins have been explored. NN127 and SQ-3 are symmetric and asymmetric squarylium dyes, respectively, the fluorescence emissions of which have been shown to be enhanced upon complexation with proteins such as bovine serum albumin (BSA), human serum albumin (HSA), beta-lactoglobulin A, and trypsinogen. Although these dyes are poorly soluble in aqueous solution, they can be dissolved first in methanol followed by dilution with aqueous buffer without precipitation, and are then suitable for use as fluorescent labels in protein determination studies. The nature of interactions between these dyes and proteins was studied using a variety of buffer systems, and it was found that electrostatic interactions are involved but not dominant. Dye/protein stoichiometries in the noncovalent complexes were found to be 1:1 for SQ-3, although various possible stoichiometries were found for NN127 depending upon pH and protein. Association constants on the order of 10(5) and 10(7) were found for noncovalent complexes of SQ-3 and NN127, respectively, with HSA, indicating stronger interactions of the symmetric dye with proteins. Finally, HSA complexes with NN127 were determined by capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). In particular, NN127 shows promise as a reagent capable of fluorescently labeling analyte proteins for analysis by CE-LIF without itself being significantly fluorescent under the aqueous solution conditions studied herein.

Overview of results from PHOBOS experiment at RHIC

NASA Astrophysics Data System (ADS)

Olszewski, Andrzej; PHOBOS Collaboration; Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Corbo, J.; Decowski, M. P.; Garcia, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Henderson, C.; Hicks, D.; Hofman, D. J.; Holzman, B.; Hollis, R. S.; Hoyński, R.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michaowski, J.; Mignerey, A. C.; Mülmenstädt, J.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Rafelski, M.; Rbeiz, M.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stephans, G. S. F.; Stodulski, M.; Sukhanov, A.; Tang, J. L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysouch, B.

2002-07-01

An overview of results for interactions of Au+Au ions at centre-of-mass energies of √sNN = 56, 130 and 200 GeV obtained by the PHOBOS collaboration at RHIC is given. Measurements of primary charged particle density near mid-rapidity indicate that particle production grows logarithmically with collision energy and faster than linearly with the number of interacting nucleons. Elliptic flow is found to be much stronger at RHIC than at SPS energy. The effect is strongest in peripheral events and decreases for more central collisions and emission angles |η| > 1. The measured anti-particle to particle ratios of production rates for pions, kaons and protons in central Au+Au interactions at √sNN = 130 GeV are compatible with the statistical model of particle production, showing an increasingly baryon-free region in mid-rapidity with the increase of collision energy.

Nuclear force from lattice QCD.

PubMed

Ishii, N; Aoki, S; Hatsuda, T

2007-07-13

The nucleon-nucleon (NN) potential is studied by lattice QCD simulations in the quenched approximation, using the plaquette gauge action and the Wilson quark action on a 32(4) [approximately (4.4 fm)(4)] lattice. A NN potential V(NN)(r) is defined from the equal-time Bethe-Salpeter amplitude with a local interpolating operator for the nucleon. By studying the NN interaction in the (1)S(0) and (3)S(1) channels, we show that the central part of V(NN)(r) has a strong repulsive core of a few hundred MeV at short distances (r approximately < 0.5 fm) surrounded by an attractive well at medium and long distances. These features are consistent with the known phenomenological features of the nuclear force.

Ionic fluids with r-6 pair interactions have power-law electrostatic screening

NASA Astrophysics Data System (ADS)

Kjellander, Roland; Forsberg, Björn

2005-06-01

The decay behaviour of radial distribution functions for large distances r is investigated for classical Coulomb fluids where the ions interact with an r-6 potential (e.g. a dispersion interaction) in addition to the Coulombic and the short-range repulsive potentials (e.g. a hard core). The pair distributions and the density-density (NN), charge-density (QN) and charge-charge (QQ) correlation functions are investigated analytically and by Monte Carlo simulations. It is found that the NN correlation function ultimately decays like r-6 for large r, just as it does for fluids of electroneutral particles interacting with an r-6 potential. The prefactor is proportional to the squared compressibility in both cases. The QN correlations decay in general like r-8 and the QQ correlations like r-10 in the ionic fluid. The average charge density around an ion decays generally like r-8 and the average electrostatic potential like r-6. This behaviour is in stark contrast to the decay behaviour for classical Coulomb fluids in the absence of the r-6 potential, where all these functions decay exponentially for large r. The power-law decays are, however, the same as for quantum Coulomb fluids. This indicates that the inclusion of the dispersion interaction as an effective r-6 interaction potential in classical systems yields the same decay behaviour for the pair correlations as in quantum ionic systems. An exceptional case is the completely symmetric binary electrolyte for which only the NN correlation has a power-law decay but not the QQ correlations. These features are shown by an analysis of the bridge function.

Unveiling magnetic interactions of ruthenium trichloride via constraining direction of orbital moments: Potential routes to realize a quantum spin liquid

NASA Astrophysics Data System (ADS)

Hou, Y. S.; Xiang, H. J.; Gong, X. G.

2017-08-01

Recent experiments reveal that the honeycomb ruthenium trichloride α -RuC l3 is a prime candidate of the Kitaev quantum spin liquid (QSL). However, there is no theoretical model which can properly describe its experimental dynamical response due to the lack of a full understanding of its magnetic interactions. Here, we propose a general scheme to calculate the magnetic interactions in systems (e.g., α -RuC l3 ) with nonnegligible orbital moments by constraining the directions of orbital moments. With this scheme, we put forward a minimal J1-K1-Γ1-J3-K3 model for α -RuC l3 and find that: (I) The third nearest neighbor (NN) antiferromagnetic Heisenberg interaction J3 stabilizes the zigzag antiferromagnetic order; (II) The NN symmetric off-diagonal exchange Γ1 plays a pivotal role in determining the preferred direction of magnetic moments and generating the spin wave gap. An exact diagonalization study on this model shows that the Kitaev QSL can be realized by suppressing the NN symmetric off-diagonal exchange Γ1 and the third NN Heisenberg interaction J3. Thus, we not only propose a powerful general scheme for investigating the intriguing magnetism of Jeff=1 /2 magnets, but also point out future directions for realizing the Kitaev QSL in the honeycomb ruthenium trichloride α -RuC l3 .

Conserved charge fluctuations using the D measure in heavy-ion collisions

NASA Astrophysics Data System (ADS)

Mishra, D. K.; Netrakanti, P. K.; Garg, P.

2017-05-01

We study the net-charge fluctuation D -measure variable, in high-energy heavy-ion collisions in heavy-ion jet interaction generator (HIJING), ultrarelativistic quantum molecular dynamics (UrQMD), and hadron resonance gas (HRG) models for various center-of-mass energies (√{sNN}). The effects of kinematic acceptance and resonance decay, in the pseudorapidity acceptance interval (Δ η ) and lower transverse momentum (pTmin) threshold, on fluctuation measures are discussed. A strong dependence of D with the Δ η in HIJING and UrQMD models is observed as opposed to results obtained from the HRG model. The dissipation of fluctuation signal is estimated by fitting the D measure as a function of the Δ η . An extrapolated function for higher Δ η values at lower √{sNN} is different from the results obtained from models. Particle species dependence of D and the effect of the pTmin selection threshold are discussed in HIJING and HRG models. The comparison of D , at midrapidity, of net-charge fluctuations at various √{sNN} obtained from the models with the data from the A Large Ion Collider Experiment (ALICE) experiment is discussed. The results from the present paper as a function of Δ η and √{sNN} will provide a baseline for comparison to experimental measurements.

{ITALIC AB INITIO} Large-Basis no-Core Shell Model and its Application to Light Nuclei

NASA Astrophysics Data System (ADS)

Barrett, Bruce R.; Navratil, Petr; Ormand, W. E.; Vary, James P.

2002-01-01

We discuss the {ITALIC ab initio} No-Core Shell Model (NCSM). In this method the effective Hamiltonians are derived microscopically from realistic nucleon-nucleon (NN) potentials, such as the CD-Bonn and the Argonne AV18 NN potentials, as a function of the finite Harmonic Oscillator (HO) basis space. We present converged results, i.e. , up to 50 Ω and 18 Ω HO excitations, respectively, for the A=3 and 4 nucleon systems. Our results for these light systems are in agreement with results obtained by other exact methods. We also calculate properties of 6Li and 6He in model spaces up to 10 Ω and of 12C up to 6 Ω. Binding energies, rms radii, excitation spectra and electromagnetic properties are discussed. The favorable comparison with available data is a consequence of the underlying NN interaction rather than a phenomenological fit.

Anxiety symptoms and quality of interaction among oncology nurses: a correlational, cross-sectional study.

PubMed

Karanikola, Maria Nk; Giannakopoulou, Margarita; Kalafati, Maria; Kaite, Charis P; Patiraki, Elisabeth; Mpouzika, Meropi; Papathanassoglou, Elisabeth E D; Middleton, Nicos

2016-01-01

To explore the severity of Anxiety Symptoms (AS) among Greek oncology nursing personnel, the degree of satisfaction from professional relationships, and potential association between them. A descriptive cross-sectional correlational study was performed in 2 Greek Oncology Hospitals, in 72 members of nursing personnel. Hamilton Anxiety Scale was used for the assessment of AS severity and the Index of Work Satisfaction subscale "Satisfaction from Interaction" for the degree of satisfaction from professional relationships among nursing personnel (NN) and between nursing personnel and physicians (NP). 11% of the sample reported clinical AS [≥26, scale range (SR): 0-52]. Satisfaction from NN [5.10 (SD: 1.04), SR: 1-7], and NP [4.21 (SD: 0.77), SR: 1-7] professional interaction were both moderate. Statistically significantly associations were observed between clinical AS and satisfaction from NN (p=0.014) and NP (p=0.013) professional interaction. Anxiety reduction interventions and improvement of professional relationships are essentials in order to reduce oncology nurses' psychological distress.

Modeling the influence of string collective phenomena on the long range rapidity correlations between the transverse momentum and the multiplicities

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

Andronov, E.; Vechernin, V.

2016-01-22

The long-range rapidity correlations between the multiplicities (n-n) and the transverse momentum and the multiplicity (pT-n) of charge particles are analyzed in the framework of the simple string inspired model with two types of sources. The sources of the first type correspond to the initial strings formed in a hadronic collision. The sources of the second type imitate the appearance of the emitters of a new kind resulting from interaction (fusion) of the initial strings. The model enabled to describe effectively the influence of the string fusion effects on the strength both the n-n and the pT-n correlations. It wasmore » found that in the region, where the process of string fusion comes into play, the calculation results predict the non-monotonic behaviour of the n-n and pT-n correlation coefficients with the growth of the mean number of initial strings, i.e. with the increase of the collision centrality. It was shown also that the increase of the event-by-event fluctuation in the number of primary strings leads to the change of the pT-n correlation sign from negative to positive. One can try to search these signatures of string collective phenomena in interactions of various nuclei at different energies varying the class of collision centrality and its width.« less

Single inclusive jet production and the nuclear modification ratio at very forward rapidity in proton-lead collisions with √{sNN } = 5.02 TeV

NASA Astrophysics Data System (ADS)

Bury, Marcin; Van Haevermaet, Hans; Van Hameren, Andreas; Van Mechelen, Pierre; Kutak, Krzysztof; Serino, Mirko

2018-05-01

We present calculations of single inclusive jet transverse momentum and energy spectra at forward rapidity (5.2 < y < 6.6) in proton-lead collisions with √{sNN } = 5.02 TeV. The predictions are obtained with the KaTie Monte Carlo event generator, which allows to calculate interactions within the High Energy Factorisation framework. The tree-level matrix element results are subsequently interfaced with the CASCADE Monte Carlo event generator to account for hadronisation. The effects of the saturation of the gluon density, leading to suppression of the cross section, are investigated.

Electric dipole moment of the deuteron in the standard model with NN - ΛN - ΣN coupling

NASA Astrophysics Data System (ADS)

Yamanaka, Nodoka

2017-07-01

We calculate the electric dipole moment (EDM) of the deuteron in the standard model with | ΔS | = 1 interactions by taking into account the NN - ΛN - ΣN channel coupling, which is an important nuclear level systematics. The two-body problem is solved with the Gaussian Expansion Method using the realistic Argonne v18 nuclear force and the YN potential which can reproduce the binding energies of Λ3H, Λ3He, and Λ4He. The | ΔS | = 1 interbaryon potential is modeled by the one-meson exchange process. It is found that the deuteron EDM is modified by less than 10%, and the main contribution to this deviation is due to the polarization of the hyperon-nucleon channels. The effect of the YN interaction is small, and treating ΛN and ΣN channels as free is a good approximation for the EDM of the deuteron.

Hierarchical modeling of molecular energies using a deep neural network

NASA Astrophysics Data System (ADS)

Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Heavy-quark production and elliptic flow in Au+Au collisions at √s NN=62.4 GeV

DOE PAGES

Adare, A.

2015-04-28

In this study, we present measurements of electrons and positrons from the semileptonic decays of heavy-flavor hadrons at midrapidity (|y|< 0.35) in Au+Au collisions at √s NN = 62.4 GeV. The data were collected in 2010 by the PHENIX experiment that included the new hadron-blind detector. The invariant yield of electrons from heavy-flavor decays is measured as a function of transverse momentum in the range 1 < p e T < 5 GeV/c. The invariant yield per binary collision is slightly enhanced above the p+p reference in Au+Au 0%–20%, 20%–40%, and 40%–60% centralities at a comparable level. At this lowmore » beam energy this may be a result of the interplay between initial-state Cronin effects, final-state flow, and energy loss in medium. The v₂ of electrons from heavy-flavor decays is nonzero when averaged between 1.3 < p e T < 2.5 GeV/c for 0%–40% centrality collisions at √s NN = 62.4 GeV. For 20%–40% centrality collisions, the v₂ at √s NN = 62.4 GeV is smaller than that for heavy-flavor decays at √s NN = 200 GeV. The v₂ of the electrons from heavy-flavor decay at the lower beam energy is also smaller than v₂ for pions. Both results indicate that the heavy-quarks interact with the medium formed in these collisions, but they may not be at the same level of thermalization with the medium as observed at √s NN = 200 GeV.« less

Delta-Isobar Production in the Hard Photodisintegration of a Deuteron

NASA Astrophysics Data System (ADS)

Granados, Carlos; Sargsian, Misak

2010-02-01

Hard photodisintegration of the deuteron in delta-isobar production channels is proposed as a useful process in identifying the quark structure of hadrons and of hadronic interactions at large momentum and energy transfer. The reactions are modeled using the hard re scattering model, HRM, following previous works on hard breakup of a nucleon nucleon (NN) system in light nuclei. Here,quantitative predictions through the HRM require the numerical input of fits of experimental NN hard elastic scattering cross sections. Because of the lack of data in hard NN scattering into δ-isobar channels, the cross section of the corresponding photodisintegration processes cannot be predicted in the same way. Instead, the corresponding NN scattering process is modeled through the quark interchange mechanism, QIM, leaving an unknown normalization parameter. The observables of interest are ratios of differential cross sections of δ-isobar production channels to NN breakup in deuteron photodisintegration. Both entries in these ratios are derived through the HRM and QIM so that normalization parameters cancel out and numerical predictions can be obtained. )

Enhancement technology improves palatability of normal and callipyge lambs.

PubMed

Everts, A K R; Wulf, D M; Wheeler, T L; Everts, A J; Weaver, A D; Daniel, J A

2010-12-01

The objective of this research was to determine if BPI Processing Technology (BPT) improved palatability of normal (NN) and callipyge (CN) lamb meat and to determine the mechanism by which palatability was improved. Ten ewe and 10 wether lambs of each phenotype were slaughtered, and carcass traits were assessed by a trained evaluator. The LM was removed at 2 d postmortem. Alternating sides served as controls (CON) or were treated with BPT. Muscles designated BPT were injected to a target 120% by weight with a patented solution containing water, ammonium hydroxide, carbon monoxide, and salt. Muscle pH, cooking loss, Warner-Bratzler shear force (WBS), sarcomere length, cooked moisture retention, and desmin degradation were measured. A trained sensory panel and a take-home consumer panel evaluated LM chops. Callipyge had a heavier BW and HCW, less adjusted fat thickness, reduced yield grades, and greater conformation scores than NN (P < 0.05). For LM, NN had shorter sarcomeres, smaller WBS values, greater juiciness ratings, more off-flavors, reduced consumer ratings for raw characteristics (like of portion size, like of color, like of leanness, overall like of appearance) and greater consumer ratings for eating characteristics (like of juiciness, like of flavor) than CN (P < 0.05). For LM, BPT had greater cooked moisture retention, smaller WBS values, greater juiciness ratings, less off-flavors, and greater consumer ratings for raw characteristics (like of portion size, like of color, overall like of appearance) and eating characteristics (like of juiciness, like of flavor) than CON (P < 0.05). Significant phenotype × treatment interactions occurred for LM muscle pH, desmin degradation, tenderness, consumer like of texture/tenderness, and consumer overall like of eating quality (P < 0.05). For LM, BPT increased muscle pH more for NN than CN (P < 0.01) and increased desmin degradation for NN but decreased desmin degradation for CN (P < 0.01). The BPT enhancement improved LM tenderness ratings for CN more than NN (P < 0.05). For consumer like of texture/tenderness, BPT improved ratings for CN more than NN (P < 0.01). For consumer overall like of eating quality, BPT improved ratings for CN more than NN (P < 0.05). In summary, BPT had little to no effect on sarcomere length and desmin degradation, but improved palatability of NN and CN lamb by increasing cooked moisture retention, improving consumer acceptability of CN to near-normal levels.

Kny Coupling Constants and Form Factors from the Chiral Bag Model

NASA Astrophysics Data System (ADS)

Jeong, M. T.; Cheon, Il-T.

2000-09-01

The form factors and coupling constants for KNΛ and KNΣ interactions have been calculated in the framework of the Chiral Bag Model with vector mesons. Taking into account vector meson (ρ, ω, K*) field effects, we find -3.88 ≤ gKNΛ ≤ -3.67 and 1.15 ≤ gKNΣ ≤ 1.24, where the quark-meson coupling constants are determined by fitting the renormalized, πNN coupling constant, [gπNN(0)]2/4π = 14.3. It is shown that vector mesons make significant contributions to the coupling constants gKNΛ and gKNΣ. Our values are existing within the experimental limits compared to the phenomenological values extracted from the kaon photo production experiments.

Rho0 production and possible modification in Au+Au and p+p collisions at square root [sNN] = 200 GeV.

PubMed

Adams, J; Adler, C; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Arkhipkin, D; Averichev, G S; Badyal, S K; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellwied, R; Berger, J; Bezverkhny, B I; Bhardwaj, S; Bhati, A K; Bichsel, H; Billmeier, A; Bland, L C; Blyth, C O; Bonner, B E; Botje, M; Boucham, A; Brandin, A; Bravar, A; Cadman, R V; Cai, X Z; Caines, H; Calderón de la Barca Sánchez, M; Carroll, J; Castillo, J; Cebra, D; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, Y; Chernenko, S P; Cherney, M; Chikanian, A; Christie, W; Coffin, J P; Cormier, T M; Cramer, J G; Crawford, H J; Das, D; Das, S; Derevschikov, A A; Didenko, L; Dietel, T; Dong, W J; Dong, X; Draper, J E; Du, F; Dubey, A K; Dunin, V B; Dunlop, J C; Dutta Majumdar, M R; Eckardt, V; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Faine, V; Faivre, J; Fatemi, R; Filimonov, K; Filip, P; Finch, E; Fisyak, Y; Flierl, D; Foley, K J; Fu, J; Gagliardi, C A; Gagunashvili, N; Gans, J; Ganti, M S; Gaudichet, L; Geurts, F; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Grachov, O; Grebenyuk, O; Gronstal, S; Grosnick, D; Guertin, S M; Gupta, A; Gutierrez, T D; Hallman, T J; Hamed, A; Hardtke, D; Harris, J W; Heinz, M; Henry, T W; Heppelmann, S; Hippolyte, B; Hirsch, A; Hjort, E; Hoffmann, G W; Horsley, M; Huang, H Z; Huang, S L; Hughes, E; Humanic, T J; Igo, G; Ishihara, A; Jacobs, P; Jacobs, W W; Janik, M; Jiang, H; Johnson, I; Jones, P G; Judd, E G; Kabana, S; Kaplan, M; Keane, D; Khodyrev, V Yu; Kiryluk, J; Kisiel, A; Klay, J; Klein, S R; Klyachko, A; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kovalenko, A D; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kunde, G J; Kunz, C L; Kutuev, R Kh; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; Lasiuk, B; Laue, F; Lauret, J; Lebedev, A; Lednický, R; LeVine, M J; Li, C; Li, Q; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, L; Liu, Z; Liu, Q J; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Lopez-Noriega, M; Love, W A; Ludlam, T; Lynn, D; Ma, J; Ma, Y G; Magestro, D; Mahajan, S; Mangotra, L K; Mahapatra, D P; Majka, R; Manweiler, R; Margetis, S; Markert, C; Martin, L; Marx, J; Matis, H S; Matulenko, Yu A; McClain, C J; McShane, T S; Meissner, F; Melnick, Yu; Meschanin, A; Miller, M L; Milosevich, Z; Minaev, N G; Mironov, C; Mischke, A; Mishra, D; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Mora-Corral, M J; Morozov, D A; Morozov, V; De Moura, M M; Munhoz, M G; Nandi, B K; Nayak, S K; Nayak, T K; Nelson, J M; Netrakanti, P K; Nikitin, V A; Nogach, L V; Norman, B; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Paic, G; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Peitzmann, T; Perevoztchikov, V; Perkins, C; Peryt, W; Petrov, V A; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rai, G; Rakness, G; Raniwala, R; Raniwala, S; Ravel, O; Ray, R L; Razin, S V; Reichhold, D; Reid, J G; Renault, G; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevski, O V; Romero, J L; Rose, A; Roy, C; Ruan, L J; Sahoo, R; Sakrejda, I; Salur, S; Sandweiss, J; Savin, I; Schambach, J; Scharenberg, R P; Schmitz, N; Schroeder, L S; Schweda, K; Seger, J; Seyboth, P; Shahaliev, E; Shao, M; Shao, W; Sharma, M; Shestermanov, K E; Shimanskii, S S; Singaraju, R N; Simon, F; Skoro, G; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Speltz, J; Spinka, H M; Srivastava, B; Stanislaus, T D S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Struck, C; Suaide, A A P; Sugarbaker, E; Suire, C; Sumbera, M; Surrow, B; Symons, T J M; Szanto de Toledo, A; Szarwas, P; Tai, A; Takahashi, J; Tang, A H; Thein, D; Thomas, J H; Timoshenko, S; Tokarev, M; Tonjes, M B; Trainor, T A; Trentalange, S; Tribble, R E; Tsai, O; Ullrich, T; Underwood, D G; Van Buren, G; VanderMolen, A M; Varma, R; Vasilevski, I; Vasiliev, A N; Vernet, R; Vigdor, S E; Viyogi, Y P; Voloshin, S A; Vznuzdaev, M; Waggoner, W; Wang, F; Wang, G; Wang, G; Wang, X L; Wang, Y; Wang, Z M; Ward, H; Watson, J W; Webb, J C; Wells, R; Westfall, G D; Whitten, C; Wieman, H; Willson, R; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Z; Xu, Z Z; Yamamoto, E; Yepes, P; Yurevich, V I; Yuting, B; Zanevski, Y V; Zhang, H; Zhang, W M; Zhang, Z P; Zhaomin, Z P; Zizong, Z P; Zołnierczuk, P A; Zoulkarneev, R; Zoulkarneeva, J; Zubarev, A N

2004-03-05

We report results on rho(770)(0)-->pi(+)pi(-) production at midrapidity in p+p and peripheral Au+Au collisions at sqrt[s(NN)]=200 GeV. This is the first direct measurement of rho(770)(0)-->pi(+)pi(-) in heavy-ion collisions. The measured rho(0) peak in the invariant mass distribution is shifted by approximately 40 MeV/c(2) in minimum bias p+p interactions and approximately 70 MeV/c(2) in peripheral Au+Au collisions. The rho(0) mass shift is dependent on transverse momentum and multiplicity. The modification of the rho(0) meson mass, width, and shape due to phase space and dynamical effects are discussed.

A software package for interactive motor unit potential classification using fuzzy k-NN classifier.

PubMed

Rasheed, Sarbast; Stashuk, Daniel; Kamel, Mohamed

2008-01-01

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

Structure and bonding in beta-HMX-characterization of a trans-annular N...N interaction.

PubMed

Zhurova, Elizabeth A; Zhurov, Vladimir V; Pinkerton, A Alan

2007-11-14

Chemical bonding in the beta-phase of the 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX) crystal based on the experimental electron density obtained from X-ray diffraction data at 20 K, and solid state theoretical calculations, has been analyzed in terms of the quantum theory of atoms in molecules. Features of the intra- and intermolecular bond critical points and the oxygen atom lone-pair locations are discussed. An unusual N...N bonding interaction across the 8-membered ring has been discovered and characterized. Hydrogen bonding, O...O and O...C intermolecular interactions are reported. Atomic charges and features of the electrostatic potential are discussed.

Anticancer Effect of Nemopilema nomurai Jellyfish Venom on HepG2 Cells and a Tumor Xenograft Animal Model

PubMed Central

Bae, Seong Kyeong; Kim, Munki; Pyo, Min Jung; Kim, Minkyung; Yang, Sujeoung; Yoon, Won Duk; Han, Chang Hoon

2017-01-01

Various kinds of animal venoms and their components have been widely studied for potential therapeutic applications. This study evaluated whether Nemopilema nomurai jellyfish venom (NnV) has anticancer activity. NnV strongly induced cytotoxicity of HepG2 cells through apoptotic cell death, as demonstrated by alterations of chromatic morphology, activation of procaspase-3, and an increase in the Bax/Bcl-2 ratio. Furthermore, NnV inhibited the phosphorylation of PI3K, PDK1, Akt, mTOR, p70S6K, and 4EBP1, whereas it enhanced the expression of p-PTEN. Interestingly, NnV also inactivated the negative feedback loops associated with Akt activation, as demonstrated by downregulation of Akt at Ser473 and mTOR at Ser2481. The anticancer effect of NnV was significant in a HepG2 xenograft mouse model, with no obvious toxicity. HepG2 cell death by NnV was inhibited by tetracycline, metalloprotease inhibitor, suggesting that metalloprotease component in NnV is closely related to the anticancer effects. This study demonstrates, for the first time, that NnV exerts highly selective cytotoxicity in HepG2 cells via dual inhibition of the Akt and mTOR signaling pathways, but not in normal cells. PMID:28785288

Centrality categorization for Rp (d)+A in high-energy collisions

NASA Astrophysics Data System (ADS)

Adare, A.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Al-Bataineh, H.; Alexander, J.; Angerami, A.; Aoki, K.; Apadula, N.; Aramaki, Y.; Atomssa, E. T.; Averbeck, R.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Baksay, G.; Baksay, L.; Barish, K. N.; Bassalleck, B.; Basye, A. T.; Bathe, S.; Baublis, V.; Baumann, C.; Bazilevsky, A.; Belikov, S.; Belmont, R.; Bennett, R.; Bhom, J. H.; Blau, D. S.; Bok, J. S.; Boyle, K.; Brooks, M. L.; Buesching, H.; Bumazhnov, V.; Bunce, G.; Butsyk, S.; Campbell, S.; Caringi, A.; Chen, C.-H.; Chi, C. Y.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choudhury, R. K.; Christiansen, P.; Chujo, T.; Chung, P.; Chvala, O.; Cianciolo, V.; Citron, Z.; Cole, B. A.; Conesa Del Valle, Z.; Connors, M.; Csanád, M.; Csörgő, T.; Dahms, T.; Dairaku, S.; Danchev, I.; Das, K.; Datta, A.; David, G.; Dayananda, M. K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Dharmawardane, K. V.; Dietzsch, O.; Dion, A.; Donadelli, M.; Drapier, O.; Drees, A.; Drees, K. A.; Durham, J. M.; Durum, A.; Dutta, D.; D'Orazio, L.; Edwards, S.; Efremenko, Y. V.; Ellinghaus, F.; Engelmore, T.; Enokizono, A.; En'yo, H.; Esumi, S.; Fadem, B.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Fraenkel, Z.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fujiwara, K.; Fukao, Y.; Fusayasu, T.; Garishvili, I.; Glenn, A.; Gong, H.; Gonin, M.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grim, G.; Grosse Perdekamp, M.; Gunji, T.; Gustafsson, H.-Å.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Hamblen, J.; Han, R.; Hanks, J.; Haslum, E.; Hayano, R.; He, X.; Heffner, M.; Hemmick, T. K.; Hester, T.; Hill, J. C.; Hohlmann, M.; Holzmann, W.; Homma, K.; Hong, B.; Horaguchi, T.; Hornback, D.; Huang, S.; Ichihara, T.; Ichimiya, R.; Ikeda, Y.; Imai, K.; Inaba, M.; Isenhower, D.; Ishihara, M.; Issah, M.; Ivanischev, D.; Iwanaga, Y.; Jacak, B. V.; Jia, J.; Jiang, X.; Jin, J.; Johnson, B. M.; Jones, T.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kajihara, F.; Kamin, J.; Kang, J. H.; Kapustinsky, J.; Karatsu, K.; Kasai, M.; Kawall, D.; Kawashima, M.; Kazantsev, A. V.; Kempel, T.; Khanzadeev, A.; Kijima, K. M.; Kikuchi, J.; Kim, A.; Kim, B. I.; Kim, D. J.; Kim, E.-J.; Kim, Y.-J.; Kinney, E.; Kiss, Á.; Kistenev, E.; Kleinjan, D.; Kochenda, L.; Komkov, B.; Konno, M.; Koster, J.; Král, A.; Kravitz, A.; Kunde, G. J.; Kurita, K.; Kurosawa, M.; Kwon, Y.; Kyle, G. S.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Lee, D. M.; Lee, J.; Lee, K. B.; Lee, K. S.; Leitch, M. J.; Leite, M. A. L.; Li, X.; Lichtenwalner, P.; Liebing, P.; Linden Levy, L. A.; Liška, T.; Liu, H.; Liu, M. X.; Love, B.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Malik, M. D.; Manko, V. I.; Mannel, E.; Mao, Y.; Masui, H.; Matathias, F.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; Means, N.; Meredith, B.; Miake, Y.; Mibe, T.; Mignerey, A. C.; Miki, K.; Milov, A.; Mitchell, J. T.; Mohanty, A. K.; Moon, H. J.; Morino, Y.; Morreale, A.; Morrison, D. P.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Nagamiya, S.; Nagle, J. L.; Naglis, M.; Nagy, M. I.; Nakagawa, I.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nam, S.; Newby, J.; Nguyen, M.; Nihashi, M.; Nouicer, R.; Nyanin, A. S.; Oakley, C.; O'Brien, E.; Oda, S. X.; Ogilvie, C. A.; Oka, M.; Okada, K.; Onuki, Y.; Orjuela Koop, J. D.; Oskarsson, A.; Ouchida, M.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, S. K.; Park, W. J.; Pate, S. F.; Pei, H.; Peng, J.-C.; Pereira, H.; Perepelitsa, D.; Peressounko, D. Yu.; Petti, R.; Pinkenburg, C.; Pisani, R. P.; Proissl, M.; Purschke, M. L.; Qu, H.; Rak, J.; Ravinovich, I.; Read, K. F.; Rembeczki, S.; Reygers, K.; Riabov, V.; Riabov, Y.; Richardson, E.; Roach, D.; Roche, G.; Rolnick, S. D.; Rosati, M.; Rosen, C. A.; Rosendahl, S. S. E.; Ružička, P.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sakashita, K.; Samsonov, V.; Sano, S.; Sato, T.; Sawada, S.; Sedgwick, K.; Seele, J.; Seidl, R.; Seto, R.; Sharma, D.; Shein, I.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Silvestre, C.; Sim, K. S.; Singh, B. K.; Singh, C. P.; Singh, V.; Slunečka, M.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Stankus, P. W.; Stenlund, E.; Stoll, S. P.; Sugitate, T.; Sukhanov, A.; Sziklai, J.; Takagui, E. M.; Taketani, A.; Tanabe, R.; Tanaka, Y.; Taneja, S.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Themann, H.; Thomas, D.; Thomas, T. L.; Togawa, M.; Toia, A.; Tomášek, L.; Torii, H.; Towell, R. S.; Tserruya, I.; Tsuchimoto, Y.; Vale, C.; Valle, H.; van Hecke, H. W.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Virius, M.; Vrba, V.; Vznuzdaev, E.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Wei, F.; Wei, R.; Wessels, J.; White, S. N.; Winter, D.; Woody, C. L.; Wright, R. M.; Wysocki, M.; Yamaguchi, Y. L.; Yamaura, K.; Yang, R.; Yanovich, A.; Ying, J.; Yokkaichi, S.; You, Z.; Young, G. R.; Younus, I.; Yushmanov, I. E.; Zajc, W. A.; Zhou, S.; Phenix Collaboration

2014-09-01

High-energy proton- and deuteron-nucleus collisions provide an excellent tool for studying a wide array of physics effects, including modifications of parton distribution functions in nuclei, gluon saturation, and color neutralization and hadronization in a nuclear environment, among others. All of these effects are expected to have a significant dependence on the size of the nuclear target and the impact parameter of the collision, also known as the collision centrality. In this article, we detail a method for determining centrality classes in p (d)+A collisions via cuts on the multiplicity at backward rapidity (i.e., the nucleus-going direction) and for determining systematic uncertainties in this procedure. For d +Au collisions at √sNN =200 GeV we find that the connection to geometry is confirmed by measuring the fraction of events in which a neutron from the deuteron does not interact with the nucleus. As an application, we consider the nuclear modification factors Rp (d)+A, for which there is a bias in the measured centrality-dependent yields owing to auto correlations between the process of interest and the backward-rapidity multiplicity. We determine the bias-correction factors within this framework. This method is further tested using the hijing Monte Carlo generator. We find that for d +Au collisions at √sNN =200 GeV, these bias corrections are small and vary by less than 5% (10%) up to pT=10 (20) GeV/c. In contrast, for p +Pb collisions at √sNN =5.02 TeV we find that these bias factors are an order of magnitude larger and strongly pT dependent, likely attributable to the larger effect of multiparton interactions.

Neutron-neutron quasifree scattering in nd breakup at 10 MeV

NASA Astrophysics Data System (ADS)

Malone, R. C.; Crowe, B.; Crowell, A. S.; Cumberbatch, L. C.; Esterline, J. H.; Fallin, B. A.; Friesen, F. Q. L.; Han, Z.; Howell, C. R.; Markoff, D.; Ticehurst, D.; Tornow, W.; Witała, H.

2016-03-01

The neutron-deuteron (nd) breakup reaction provides a rich environment for testing theoretical models of the neutron-neutron (nn) interaction. Current theoretical predictions based on rigorous ab-initio calculations agree well with most experimental data for this system, but there remain a few notable discrepancies. The cross section for nn quasifree (QFS) scattering is one such anomaly. Two recent experiments reported cross sections for this particular nd breakup configuration that exceed theoretical calculations by almost 20% at incident neutron energies of 26 and 25 MeV [1, 2]. The theoretical values can be brought into agreement with these results by increasing the strength of the 1S0 nn potential matrix element by roughly 10%. However, this modification of the nn effective range parameter and/or the 1S0 scattering length causes substantial charge-symmetry breaking in the nucleon-nucleon force and suggests the possibility of a weakly bound di-neutron state [3]. We are conducting new measurements of the cross section for nn QFS in nd breakup. The measurements are performed at incident neutron beam energies below 20 MeV. The neutron beam is produced via the 2H(d, n)3He reaction. The target is a deuterated plastic cylinder. Our measurements utilize time-of-flight techniques with a pulsed neutron beam and detection of the two emitted neutrons in coincidence. A description of our initial measurements at 10 MeV for a single scattering angle will be presented along with preliminary results. Also, plans for measurements at other energies with broad angular coverage will be discussed.

Nuclear physics with antiprotons

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

Dover, C.B.

1984-01-01

Transparencies of an invited talk presented at the Nashville meeting of the American Physical Society, October 18-20, 1984, are included. Topics include: (1) Salient features of two-body N anti N interactions (N anti N reversible NN, annihilation mechanisms (quark models), and optical model phenomenology); (2) anti N-nucleus interactions - elastic, inelastic, etc. (new cross section data, optical potentials, signatures of spin-isospin dependence of N anti N force, and (anti p, p) reactions); and (3) anti N-nucleus annihilation processes (features of cascade or fluid dynamics calculations, searches for baryonium and other exotics, meson interferometry, and (anti p, NN) reactions. (WHK)

Effects of temperature, algae biomass and ambient nutrient on the absorption of dissolved nitrogen and phosphate by Rhodophyte Gracilaria asiatica

NASA Astrophysics Data System (ADS)

Du, Rongbin; Liu, Liming; Wang, Aimin; Wang, Yongqiang

2013-03-01

Gracilaria asiatica, being highly efficient in nutrient absorption, is cultivated in sea cucumber ponds to remove nutrients such as nitrogen and phosphate. It was cultured in a laboratory simulating field conditions, and its nutrient absorption was measured to evaluate effects of environmental conditions. Ammonia nitrogen (AN), nitrate nitrogen (NN), total inorganic nitrogen (TIN), and soluble reactive phosphorus (SRP) uptake rate and removal efficiency were determined in a 4×2 factorial design experiment in water temperatures ( T) at 15°C and 25°C, algae biomass (AB) at 0.5 g/L and 1.0 g/L, total inorganic nitrogen (TIN) at 30 μmol/L and 60 μmol/L, and soluble reactive phosphorus (SRP) at 3 and 6 μmol/L. AB and ambient TIN or SRP levels significantly affected uptake rate and removal efficiency of AN, NN, TIN, and SRP ( P< 0.001). G. asiatica in AB of 0.5 g/L showed higher uptake rate and lower removal efficiency relative to that with AB of 1.0 g/L. Nitrogen and phosphorus uptake rate rose with increasing ambient nutrient concentrations; nutrient removal efficiency decreased at higher environmental nutrient concentrations. The algae preferred to absorb AN to NN. Uptake rates of AN, NN, and SRP were significantly affected by temperature ( P < 0.001); uptake rate was higher for the 25°C group than for the 15°C group at the initial experiment stage. Only the removal efficiency of AN and SRP showed a significant difference between the two temperature groups ( P< 0.01). The four factors had significant interactive effects on absorption of N and P, implying that G. asiatica has great bioremedial potential in sea cucumber culture ponds.

Neural networks: further insights into error function, generalized weights and others

PubMed Central

2016-01-01

The article is a continuum of a previous one providing further insights into the structure of neural network (NN). Key concepts of NN including activation function, error function, learning rate and generalized weights are introduced. NN topology can be visualized with generic plot() function by passing a “nn” class object. Generalized weights assist interpretation of NN model with respect to the independent effect of individual input variables. A large variance of generalized weights for a covariate indicates non-linearity of its independent effect. If generalized weights of a covariate are approximately zero, the covariate is considered to have no effect on outcome. Finally, prediction of new observations can be performed using compute() function. Make sure that the feature variables passed to the compute() function are in the same order to that in the training NN. PMID:27668220

A silk peptide fraction restores cognitive function in AF64A-induced Alzheimer disease model rats by increasing expression of choline acetyltransferase gene

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

Cha, Yeseul

This study investigated the effects of a silk peptide fraction obtained by incubating silk proteins with Protease N and Neutrase (SP-NN) on cognitive dysfunction of Alzheimer disease model rats. In order to elucidate underlying mechanisms, the effect of SP-NN on the expression of choline acetyltransferase (ChAT) mRNA was assessed in F3.ChAT neural stem cells and Neuro2a neuroblastoma cells; active amino acid sequence was identified using HPLC-MS. The expression of ChAT mRNA in F3.ChAT cells increased by 3.79-fold of the control level by treatment with SP-NN fraction. The active peptide in SP-NN was identified as tyrosine-glycine with 238.1 of molecular weight.more » Male rats were orally administered with SP-NN (50 or 300 mg/kg) and challenged with a cholinotoxin AF64A. As a result of brain injury and decreased brain acetylcholine level, AF64A induced astrocytic activation, resulting in impairment of learning and memory function. Treatment with SP-NN exerted recovering activities on acetylcholine depletion and brain injury, as well as cognitive deficit induced by AF64A. The results indicate that, in addition to a neuroprotective activity, the SP-NN preparation restores cognitive function of Alzheimer disease model rats by increasing the release of acetylcholine. - Highlights: • Cognition-enhancing effects of SP-NN, a silk peptide preparation, were investigated. • SP-NN enhanced ChAT mRNA expression in F3.ChAT neural stem cells and Neuro-2a neuroblastoma cells. • Active molecule was identified as a dipeptide composed of tyrosine-glycine. • SP-NN reversed cognitive dysfunction elicited by AF64A. • Neuroprotection followed by increased acetylcholine level was achieved with SP-NN.« less

Proton Radii of 4,6,8He Isotopes from High-Precision Nucleon-Nucleon Interactions

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

Caurier, E; Navratil, P

2005-11-16

Recently, precision laser spectroscopy on {sup 6}He atoms determined accurately the isotope shift between {sup 4}He and {sup 6}He and, consequently, the charge radius of {sup 6}He. A similar experiment for {sup 8}He is under way. We have performed large-scale ab initio calculations for {sup 4,6,8}He isotopes using high-precision nucleon-nucleon (NN) interactions within the no-core shell model (NCSM) approach. With the CD-Bonn 2000 NN potential we found point-proton root-mean-square (rms) radii of {sup 4}He and {sup 6}He 1.45(1) fm and 1.89(4), respectively, in agreement with experiment and predict the {sup 8}He point proton rms radius to be 1.88(6) fm. Atmore » the same time, our calculations show that the recently developed nonlocal INOY NN potential gives binding energies closer to experiment, but underestimates the charge radii.« less

Quantum mechanics study of repulsive π-π interaction and flexibility of phenyl moiety in the iron azodioxide complex

NASA Astrophysics Data System (ADS)

Liu, Yuemin; Liu, Yucheng; Murru, Siva; Tzeng, Nianfeng; Srivastava, Radhey S.

2015-10-01

In this study, repulsive π-π interactions within iron azodioxide complex Fe[Ph(O)NN(O)Ph]3 were quantum mechanically characterized using DFT, MP2 and CCSD(T) methods. Flexibility of six phenyl moieties in this complex structure was also investigated by structural optimization approach using the DFT methods. Our MP2 and CCSD(T) calculations of the closest pair provided interaction energy of 6.62 and 8.29 kcal/mol respectively, which indicate a strongest repulsion among these intra-molecular π-π interactions. Interaction energy of the particular π-π pair calculated from 24 hybrid DFT methods ranges from 4.56 kcal/mol from BHandH method to 15.15 kcal/mol from O3LYP method. Cares should be exercised when interpreting interaction energy and geometry optimization from DFT simulation of systems containing π-π interaction. Comparison between the DFT results and the benchmark CCSD(T) results shows that the DFT calculations of π-π interaction are reasonable but still need to be interpreted with caution. Furthermore, MP2 interaction energy of -44.69 kcal/mol between two substituted π systems/phenyl rings Ph(O)N-moieties suggested that above energetically unfavorable π-π interaction can be compensated by the covalent bond N-N in a single ligand Ph(O)NN(O)Ph, which allows for a reasonable stability across the complex molecules. Optimizations of the entire complex molecule using B3LYP and M06HF methods produced a large variation of π-π distances and orientations, which implied that the complex molecule may perform catalysis at room temperature.

Exceptionally long-lived light-emitting electrochemical cells: multiple intra-cation π-stacking interactions in [Ir(C^N)2(N^N)][PF6] emitters† †Electronic supplementary information (ESI) available: Experimental details. Fig. S1–S10: additional structural and NMR spectroscopic figures; voltage vs. time plots for LEC devices and electroluminescence spectra. CCDC 1019226–1019229. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/c4sc03942d

PubMed Central

Bünzli, Andreas M.; Constable, Edwin C.; Prescimone, Alessandro; Zampese, Jennifer A.; Longo, Giulia; Gil-Escrig, Lidón; Pertegás, Antonio; Ortí, Enrique

2015-01-01

A series of cyclometalated iridium(iii) complexes [Ir(C^N)2(N^N)][PF6] (N^N = 2,2′-bipyridine (1), 6-phenyl-2,2′-bipyridine (2), 4,4′-di-tert-butyl-2,2′-bipyridine (3), 4,4′-di-tert-butyl-6-phenyl-2,2′-bipyridine (4); HC^N = 2-(3-phenyl)phenylpyridine (HPhppy) or 2-(3,5-diphenyl)phenylpyridine (HPh2ppy)) are reported. They have been synthesized using solvento precursors so as to avoid the use of chlorido-dimer intermediates, chloride ion contaminant being detrimental to the performance of [Ir(C^N)2(N^N)][PF6] emitters in light-electrochemical cell (LEC) devices. Single crystal structure determinations and variable temperature solution 1H NMR spectroscopic data confirm that the pendant phenyl domains engage in multiple face-to-face π-interactions within the coordination sphere of the iridium(iii) centre. The series of [Ir(Phppy)2(N^N)]+ and [Ir(Ph2ppy)2(N^N)]+ complexes investigated include those with and without intra-cation face-to-face π-stacking. All the complexes display excellent luminescent properties, in particular when employed in thin solid films. The most important observation is that all the LECs using the [Ir(Phppy)2(N^N)]+ and [Ir(Ph2ppy)2(N^N)]+ emitters (i.e. with and without intra-cation π-stacking interactions) exhibit very stable luminance outputs over time, even when driven at elevated current densities. The most stable LEC had an extrapolated lifetime of more than 2500 hours under accelerated testing conditions. PMID:29142683

Pharmacological characterisation of a new oral GH secretagogue, NN703.

PubMed

Hansen, B S; Raun, K; Nielsen, K K; Johansen, P B; Hansen, T K; Peschke, B; Lau, J; Andersen, P H; Ankersen, M

1999-08-01

NN703 is a novel orally active GH secretagogue (GHS) derived from ipamorelin. NN703 stimulates GH release from rat pituitary cells in a dose-dependent manner with a potency and efficacy similar to that of GHRP-6. The effect is inhibited by known GHS antagonists, but not by a GH-releasing hormone antagonist. Binding of (35)S-MK677 to the human type 1A GHS receptor (GHS-R 1A) stably expressed on BHK cells was inhibited by GHRP-6 and MK677 as expected. NN703 was also able to inhibit the binding of (35)S-MK677. However, the observed K(i) value was lower than expected, as based on the observed potencies regarding GH release from rat pituitary cells. Similarly, the effect of NN703 on the GHS-R 1A-induced inositol phosphate turnover in these cells showed a lower potency, when compared with GHRP-6 and MK677, than that observed in rat pituitary cells. The effect of i.v. administration of NN703 on GH and cortisol release was studied in swine. The potency and efficacy of NN703 on GH release were determined to be 155+/-23 nmol/kg and 91+/-7 ng GH/ml plasma respectively. A 50% increase of cortisol, compared with basal levels, was observed for all the tested doses of NN703, but no dose-dependency was shown. The effect of NN703 on GH release after i. v. and oral dosing in beagle dogs was studied. NN703 dose-dependently increased the GH release after oral administration. At the highest dose (20 micromol/kg), a 35-fold increase in peak GH concentration was observed (49.5+/-17.8 ng/ml, mean+/-s.e.m.). After a single i.v. dose of 1 micromol/kg the peak GH plasma concentration was elevated to 38.5+/-19.6 ng/ml (mean+/-s.e.m.) approximately 30 min after dosing and returned to basal level after 360 min. The oral bioavailability was 30%. The plasma half-life of NN703 was 4.1+/-0.4 h. A long-term biological effect of NN703 was demonstrated in a rat study, where the body weight gain was measured during a 14-day once daily oral challenge with 100 micromol/kg. The body weight gain was significantly increased after 14 days as compared with a vehicle-treated group. In summary, we here describe an orally active and GH specific secretagogue, NN703. This compound acts through a similar mechanism as GHRP-6, but has a different receptor pharmacology. NN703 induced GH release in both swine and dogs after i.v. and/or p.o. administration, had a high degree of GH specificity in swine and significantly increased the body weight gain in rats.

Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering

NASA Astrophysics Data System (ADS)

Razavi, S.; Tolson, B.; Burn, D.; Seglenieks, F.

2012-04-01

Reformulated Neural Network (ReNN) has been recently developed as an efficient and more effective alternative to feedforward multi-layer perceptron (MLP) neural networks [Razavi, S., and Tolson, B. A. (2011). "A new formulation for feedforward neural networks." IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169]. This presentation initially aims to introduce the ReNN to the water resources community and then demonstrates ReNN applications to water resources related problems. ReNN is essentially equivalent to a single-hidden-layer MLP neural network but defined on a new set of network variables which is more effective than the traditional set of network weights and biases. The main features of the new network variables are that they are geometrically interpretable and each variable has a distinct role in forming the network response. ReNN is more efficiently trained as it has a less complex error response surface. In addition to the ReNN training efficiency, the interpretability of the ReNN variables enables the users to monitor and understand the internal behaviour of the network while training. Regularization in the ReNN response can be also directly measured and controlled. This feature improves the generalization ability of the network. The appeal of the ReNN is demonstrated with two ReNN applications to water resources engineering problems. In the first application, the ReNN is used to model the rainfall-runoff relationships in multiple watersheds in the Great Lakes basin located in northeastern North America. Modelling inflows to the Great Lakes are of great importance to the management of the Great Lakes system. Due to the lack of some detailed physical data about existing control structures in many subwatersheds of this huge basin, the data-driven approach to modelling such as the ReNN are required to replace predictions from a physically-based rainfall runoff model. Unlike traditional MLPs, the ReNN does not necessarily require an independent set of data for validation as the ReNN has the capability to control and verify the network degree of regularization. As such, the ReNN can be very beneficial in this case study as the data available in this case study is limited. In the second application, ReNN is fitted on the response function of the SWAT hydrologic model to act as a fast-to-run response surface surrogate (i.e., metamodel) of the original computationally intensive SWAT model. Besides the training efficiency gains, the ReNN applications demonstrate how the ReNN interpretability could help users develop more reliable networks which perform predictably better in terms of generalization.

Tensor Target Spin Asymmetries in Coherent π 0-Photoproduction on the Deuteron Including Intermediate η N N Interaction Within a Three-Body Approach

NASA Astrophysics Data System (ADS)

Darwish, Eed M.; Abou-Elsebaa, Hoda M.; Hassaneen, Khaled S. A.

2018-04-01

Motivated by the recent measurements from the VEPP-3 electron storage ring, we investigate the tensor target polarization asymmetries T 2 M ( M = 0, 1, 2) in the reaction γ d → π 0 d with a particular interest in the effect of the intermediate η N N three-body approach. This approach is based on realistic separable representations of the driving two-body interaction in the π N, η N, and NN subsystems. It is shown that the influence of rescattering effects in the intermediate state on the tensor target spin asymmetries is sizable at extreme backward pion angles. At forward angles, the contribution from the pure impulse approximation is dominated and the spin asymmetries show very little influence of rescattering effects. The sensitivity of results to the elementary pion photoproduction operator and to the NN potential model adopted for the deuteron wave function is investigated, and considerable dependences are found. The predicted spin asymmetries are also compared with available experimental data, and a satisfactory agreement with the recent data from VEPP-3 is obtained at photon energies below 400 MeV. At higher energies, the calculated spin asymmetries slightly underestimate the data.

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Similarity-transformed chiral NN + 3N interactions for the ab initio description of 12C and 16O.

PubMed

Roth, Robert; Langhammer, Joachim; Calci, Angelo; Binder, Sven; Navrátil, Petr

2011-08-12

We present first ab initio no-core shell model (NCSM) calculations using similarity renormalization group (SRG) transformed chiral two-nucleon (NN) plus three-nucleon (3N) interactions for nuclei throughout the p-shell, particularly (12)C and (16)O. By introducing an adaptive importance truncation for the NCSM model space and an efficient JT-coupling scheme for the 3N matrix elements, we are able to surpass previous NCSM studies including 3N interactions. We present ground and excited states in (12)C and (16)O for model spaces up to N(max) = 12 including full 3N interactions. We analyze the contributions of induced and initial 3N interactions and probe induced 4N terms through the sensitivity of the energies on the SRG flow parameter. Unlike for light p-shell nuclei, SRG-induced 4N contributions originating from the long-range two-pion terms of the chiral 3N interaction are sizable in (12)C and (16)O.

Effect of 0.25 and 2.0 MeV He-Ion Irradiation on Short-Range Ordering in Model (EFDA) Fe-Cr Alloys

NASA Astrophysics Data System (ADS)

Dubiel, Stanisław M.; Żukrowski, Jan; Serruys, Yves

2018-05-01

The effects of He+ irradiation on a distribution of Cr atoms in Fe100-x Cr x (x = 5.8, 10.75, 15.15) alloys were studied by 57Fe Conversion Electron Mössbauer Spectroscopy (CEMS). The alloys were irradiated with doses up to 12 × 1016 ions/cm2 with 0.25 and 2.0 MeV He+ ions. The distribution of Cr atoms within the first two coordination shells around Fe atoms was expressed with short-range order parameters α 1 (first-neighbor shell, 1NN), α 2 (second-neighbor shell, 2NN), and α 12 (1NN + 2NN). In non-irradiated alloys, α 1 >0 and α 2 <0 was revealed for all three samples. The value of α 12 ≈0, i.e., the distribution of Cr atoms averaged over 1NN and 2NN, was random. The effect of the irradiation of the Fe94.2Cr5.8 alloy was similar for the two energies of He+, viz., increase of number of Cr atoms in 1NN and decrease in 2NN. Consequently, the degree of ordering increased. For the other two samples, the effect of the irradiation depends on the composition, and is stronger for the less energetic ions where, for Fe89.25Cr10.75 alloy, the disordering disappeared and some traces of Cr clustering appeared. In Fe84.85Cr15.15 alloy, the clustering was clear. In the samples irradiated with 2. 0 MeV He+ ions, the ordering also survived in the samples with x = 10.75 and 15.15, yet its degree became smaller than in the Fe94.2Cr5.8 alloy.

Spin-Flavor van der Waals Forces and NN interaction

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

Alvaro Calle Cordon, Enrique Ruiz Arriola

A major goal in Nuclear Physics is the derivation of the Nucleon-Nucleon (NN) interaction from Quantum Chromodynamics (QCD). In QCD the fundamental degrees of freedom are colored quarks and gluons which are confined to form colorless strongly interacting hadrons. Because of this the resulting nuclear forces at sufficiently large distances correspond to spin-flavor excitations, very much like the dipole excitations generating the van der Waals (vdW) forces acting between atoms. We study the Nucleon-Nucleon interaction in the Born-Oppenheimer approximation at second order in perturbation theory including the Delta resonance as an intermediate state. The potential resembles strongly chiral potentials computedmore » either via soliton models or chiral perturbation theory and has a van der Waals like singularity at short distances which is handled by means of renormalization techniques. Results for the deuteron are discussed.« less

Neural network potential for Al-Mg-Si alloys

NASA Astrophysics Data System (ADS)

Kobayashi, Ryo; Giofré, Daniele; Junge, Till; Ceriotti, Michele; Curtin, William A.

2017-10-01

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

Anion induced conformational preference of Cα NN motif residues in functional proteins.

PubMed

Patra, Piya; Ghosh, Mahua; Banerjee, Raja; Chakrabarti, Jaydeb

2017-12-01

Among different ligand binding motifs, anion binding C α NN motif consisting of peptide backbone atoms of three consecutive residues are observed to be important for recognition of free anions, like sulphate or biphosphate and participate in different key functions. Here we study the interaction of sulphate and biphosphate with C α NN motif present in different proteins. Instead of total protein, a peptide fragment has been studied keeping C α NN motif flanked in between other residues. We use classical force field based molecular dynamics simulations to understand the stability of this motif. Our data indicate fluctuations in conformational preferences of the motif residues in absence of the anion. The anion gives stability to one of these conformations. However, the anion induced conformational preferences are highly sequence dependent and specific to the type of anion. In particular, the polar residues are more favourable compared to the other residues for recognising the anion. © 2017 Wiley Periodicals, Inc.

system

NASA Astrophysics Data System (ADS)

Garcilazo, H.; Valcarce, A.; Vijande, J.

2017-07-01

Using local central Yukawa-type Malfliet-Tjon interactions reproducing the low-energy parameters and phase shifts of the nn system, and the latest updates of the nΛ and ΛΛ Nijmegen ESC08c potentials, we study the possible existence of a bound state. Our results indicate that the is unbound, being just above threshold. We discuss the role played by the 1 S 0 nn repulsive term of the Yukawa-type Malfliet-Tjon interaction. Supported by COFAA-IPN (México), Ministerio de Economía, Industria y Competitividad and EU FEDER (FPA2013-47443, FPA2015-69714-REDT, FPA2016-77177), Junta de Castilla y León (SA041U16) and Generalitat Valenciana PrometeoII/2014/066

Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

PubMed Central

CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

2014-01-01

Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

Measurement of charged-particle spectra in Pb+Pb collisions at \\( \\sqrt{s_{\\mathrm{NN}}}=2.76 \\) TeV with the ATLAS detector at the LHC

DOE PAGES

Aad, G.

2015-09-09

Charged-particle spectra obtained in Pb+Pb interactions at \\( \\sqrt{s_{\\mathrm{NN}}}=2.76 \\) TeV and pp interactions at \\( \\sqrt{s_{\\mathrm{NN}}}=2.76 \\) TeV with the ATLAS detector at the LHC are presented, using data with integrated luminosities of 0.15 nb⁻¹ and 4.2 pb⁻¹, respectively, in a wide transverse momentum (0.5 < p T < 150 GeV) and pseudorapidity (|η| < 2) range. For Pb+Pb collisions, the spectra are presented as a function of collision centrality, which is determined by the response of the forward calorimeters located on both sides of the interaction point. The nuclear modification factors R AA and R CP are presentedmore » in detail as a function of centrality, p T and η. They show a distinct p T-dependence with a pronounced minimum at about 7 GeV. Above 60 GeV, R AA is consistent with a plateau at a centrality-dependent value, within the uncertainties. The value is 0.55 ± 0.01(stat.) ± 0.04(syst.) in the most central collisions. The R AA distribution is consistent with flat |η| dependence over the whole transverse momentum range in all centrality classes.« less

Measurement of charged-particle spectra in Pb+Pb collisions at √{s_{NN}}=2.76 TeV with the ATLAS detector at the LHC

NASA Astrophysics Data System (ADS)

Aad, G.; Abbott, B.; Abdallah, J.; Abdel Khalek, S.; Abdinov, O.; Aben, R.; Abi, B.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Agatonovic-Jovin, T.; Aguilar-Saavedra, J. A.; Agustoni, M.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimoto, G.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexandre, G.; Alexopoulos, T.; Alhroob, M.; Alimonti, G.; Alio, L.; Alison, J.; Allbrooke, B. M. M.; Allison, L. J.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Altheimer, A.; Alvarez Gonzalez, B.; Alviggi, M. G.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Anduaga, X. S.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnal, V.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Auerbach, B.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Bacci, C.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J. T.; Baker, O. K.; Balek, P.; Balestri, T.; Balli, F.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Bansil, H. S.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, S.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, P. J.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez Garcia, J. A.; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Beringer, J.; Bernard, C.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertsche, C.; Bertsche, D.; Besana, M. I.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Bieniek, S. P.; Biglietti, M.; Bilbao De Mendizabal, J.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boddy, C. R.; Boehler, M.; Bogaerts, J. A.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. 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F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, F. E.; Taylor, G. N.; Taylor, W.; Teischinger, F. A.; Teixeira Dias Castanheira, M.; Teixeira-Dias, P.; Temming, K. K.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Therhaag, J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Thong, W. M.; Thun, R. P.; Tian, F.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tiouchichine, E.; Tipton, P.; Tisserant, S.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tollefson, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Tran, H. L.; Trefzger, T.; Tremblet, L.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; True, P.; Trzebinski, M.; Trzupek, A.; Tsarouchas, C.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsionou, D.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tykhonov, A.; Tylmad, M.; Tyndel, M.; Ueda, I.; Ueno, R.; Ughetto, M.; Ugland, M.; Uhlenbrock, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Valladolid Gallego, E.; Vallecorsa, S.; Valls Ferrer, J. A.; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; Van Der Leeuw, R.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vannucci, F.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veloso, F.; Velz, T.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigne, R.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Virzi, J.; Vivarelli, I.; Vives Vaque, F.; Vlachos, S.; Vladoiu, D.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Warsinsky, M.; Washbrook, A.; Wasicki, C.; Watkins, P. M.; Watson, A. T.; Watson, I. J.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wendland, D.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Wharton, A. M.; White, A.; White, M. J.; White, R.; White, S.; Whiteson, D.; Wicke, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, A.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yakabe, R.; Yamada, M.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yanush, S.; Yao, L.; Yao, W.-M.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yurkewicz, A.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, R.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zurzolo, G.; Zwalinski, L.

2015-09-01

Charged-particle spectra obtained in Pb+Pb interactions at √{s_{NN}}=2.76 TeV and pp interactions at √{s_{NN}}=2.76 TeV with the ATLAS detector at the LHC are presented, using data with integrated luminosities of 0.15 nb-1 and 4.2 pb-1, respectively, in a wide transverse momentum (0 .5 < p T < 150 GeV) and pseudorapidity (| η| < 2) range. For Pb+Pb collisions, the spectra are presented as a function of collision centrality, which is determined by the response of the forward calorimeters located on both sides of the interaction point. The nuclear modification factors R AA and R CP are presented in detail as a function of centrality, p T and η. They show a distinct p T-dependence with a pronounced minimum at about 7 GeV. Above 60 GeV, R AA is consistent with a plateau at a centrality-dependent value, within the uncertainties. The value is 0 .55 ± 0 .01(stat .) ± 0 .04(syst .) in the most central collisions. The R AA distribution is consistent with flat | η| dependence over the whole transverse momentum range in all centrality classes. [Figure not available: see fulltext.

Measurement of charged-particle spectra in Pb+Pb collisions at s N N = 2.76 $$ \\sqrt{s_{\\mathrm{NN}}}=2.76 $$ TeV with the ATLAS detector at the LHC

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

Aad, G.; Abbott, B.; Abdallah, J.

2015-09-01

Charged-particle spectra obtained in Pb+Pb interactions at √s NN=2.76 and pp interactions at √s NN=2.76 TeV with the ATLAS detector at the LHC are presented, using data with integrated luminosities of 0.15 nb -1 and 4.2 pb -1, respectively, in a wide transverse momentum (0.5 < p T < 150 GeV) and pseudorapidity (|η| < 2) range. For Pb+Pb collisions, the spectra are presented as a function of collision centrality, which is determined by the response of the forward calorimeters located on both sides of the interaction point. The nuclear modification factors R AA and R CP are presented inmore » detail as a function of centrality, p T and η. They show a distinct p T-dependence with a pronounced minimum at about 7 GeV. Above 60 GeV, R AA is consistent with a plateau at a centrality-dependent value, within the uncertainties. The value is 0.55 ± 0.01(stat.) ± 0.04(syst.) in the most central collisions. The R AA distribution is consistent with flat |η| dependence over the whole transverse momentum range in all centrality classes.« less

The standard aqueous stem bark extract of Mangifera indica L. inhibits toxic PLA2 - NN-XIb-PLA2 of Indian cobra venom.

PubMed

Dhananjaya, Bhadrapura Lakkappa; Sudarshan, Shivalingaiah; Dongol, Yashad; More, Sunil S

2016-05-01

The aqueous extract of Mangifera indica is known to possess diverse medicinal properties, which also includes anti-snake venom activities. However, its inhibitory potency and mechanism of action on multi-toxic snake venom phospholipases A2s are still unknown. Therefore, the objective of this study was to evaluate the modulatory effect of standard aqueous bark extract of M. indica on NN-XIb-PLA2 of Indian cobra venom. The in vitro sPLA2, in situ hemolytic and in vivo edema inhibition effect were carried out as described. Also the effect of substrate and calcium concentration was carried out. M. indica extract dose dependently inhibited the GIA sPLA2 (NN-XIb-PLA2) activity with an IC50 value of 7.6 μg/ml. M. indica extract effectively inhibited the indirect hemolytic activity up to 98% at ∼40 μg/ml concentration. Further, M. indica extract (0-50 μg/ml) inhibited the edema formed in a dose dependent manner. When examined as a function of increased substrate and calcium concentration, there was no relieve of inhibitory effect of M. indica extract on the NN-XIb-PLA2. Further, the inhibition was irreversible as evident from binding studies. The in vitro inhibition is well correlated with in situ and in vivo edema inhibiting activities of M. indica. As the inhibition is independent of substrate and calcium and was irreversible, it can be concluded that M. indica extract mode of inhibition could be due to direct interaction of components present in the extract with the PLA2 enzyme. The aqueous extract of M. indica effectively inhibits svPLA2 enzymatic and its associated toxic activities, which substantiate their anti-snake venom properties. Further in-depth studies on the role and mechanism of the principal constituents present in the extract, responsible for the anti-PLA2 activity will be interesting to develop them into potent antisnake component and also as an anti-inflammatory agent.

A polyethylenimine-mimetic biodegradable polycation gene vector and the effect of amine composition in transfection efficiency.

PubMed

Shen, J; Zhao, D J; Li, W; Hu, Q L; Wang, Q W; Xu, F J; Tang, G P

2013-06-01

The low toxicity and efficient gene delivery of polymeric vectors remain the major barrier to the clinical application of non-viral gene therapy. Here, we present a poly-D, L-succinimide (PSI)-based biodegradable cationic polymer which mimicked the golden standard, branched polyethylenimine (PEI, ~25 kDa). To investigate the influence of 1°, 2°, 3° amine group ratio in the polymer, a series of PSI-based vectors (PSI-NN'x-NNy) grafted with different amine side chains of N,N-dimethyldipropylenetriamine (NN') and bis(3-aminopropyl)amine (NN) were first characterized and contrasted by biophysical measurements. The in vitro and in vivo biological assay demonstrated that PSI-NN'0.85-NN1 exhibited better transfection ability and biocompatibility than PEI. The present results suggest that such PEI-mimic biodegradable PSI-NN'0.85-NN1 possesses a good potential application for clinical gene delivery. Copyright © 2013 Elsevier Ltd. All rights reserved.

Measurement of the Parity-Violating Neutron Spin Rotation in 4He

PubMed Central

Bass, C. D.; Dawkins, J. M.; Luo, D.; Micherdzinska, A.; Sarsour, M.; Snow, W. M.; Mumm, H. P.; Nico, J. S.; Huffman, P. R.; Markoff, D. M.; Heckel, B. R.; Swanson, H. E.

2005-01-01

In the meson exchange model of weak nucleon-nucleon (NN) interactions, the exchange of virtual mesons between the nucleons is parameterized by a set of weak meson exchange amplitudes. The strengths of these amplitudes from theoretical calculations are not well known, and experimental measurements of parity-violating (PV) observables in different nuclear systems have not constrained their values. Transversely polarized cold neutrons traveling through liquid helium experience a PV spin rotation due to the weak interaction with an angle proportional to a linear combination of these weak meson exchange amplitudes. A measurement of the PV neutron spin rotation in helium (φPV (n,α)) would provide information about the relative strengths of the weak meson exchange amplitudes, and with the longitudinal analyzing power measurement in the p + α system, allow the first comparison between isospin mirror systems in weak NN interaction. An earlier experiment performed at NIST obtained a result consistent with zero: φPV (n,α) = (8.0 ±14(stat) ±2.2(syst)) ×10−7 rad / m[1]. We describe a modified apparatus using a superfluid helium target to increase statistics and reduce systematic effects in an effort to reach a sensitivity goal of 10−7 rad/m. PMID:27308122

High-Power, High-Intensity Laser Propagation and Interactions

DTIC Science & Technology

2014-03-10

wave Brillouin mixing [89,90]. transmitted beam is phase conjugated target initial wave front nn  1 turbulent air Figure 14. Using phase and...discussed in connection with both high-power and high-intensity lasers is propagation in a turbulent atmosphere . Laser propagation in atmospheric ... turbulence can results in beam centroid wander, spreading and intensity scintillation. A phase conjugation technique to mitigate the effects of atmospheric

Interpretation of STS-3/plasma diagnostics package results in terms of large space structure plasma interactions

NASA Technical Reports Server (NTRS)

Kurth, W. S.

1984-01-01

The Plasma Diagnostics Package, which was flown aboard STS-3 recorded various chemical releases from the Orbiter. Changes in the plasma environment were observed to occur during Flash Evaporator System (FES) releases, water dumps and maneuvering thruster operations. During flash evaporator operations, broadband Orbiter-generated electro-static noise is enhanced and plasma density irregularity (delta n/N) is observed to increase by as much as 4 times and is strongly peaked below 6 Hz. In the case of water dumps, background electrostatic noise is enhanced or suppressed depending on frequency and Delta N/N is also seen to increase by as much as 4 times. Various changes in the plasma environment are effected by primary and vernier thruster operations. In addition, thruster activity stimulates electrostatic noise with a spectrum which is most intense at frequencies below 10 kHz.

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

Bayar, M.; Yamagata-Sekihara, J.; Oset, E.

We have performed a calculation of the scattering amplitude for the three-body system KNN assuming K scattering against a NN cluster using the fixed center approximation to the Faddeev equations. The KN amplitudes, which we take from chiral unitary dynamics, govern the reaction and we find a KNN amplitude that peaks around 40 MeV below the KNN threshold, with a width in |T|{sup 2} of the order of 50 MeV for spin 0 and has another peak around 27 MeV with similar width for spin 1. The results are in line with those obtained using different methods but implementing chiralmore » dynamics. The simplicity of the approach allows one to see the important ingredients responsible for the results. In particular, we show the effects from the reduction of the size of the NN cluster due to the interaction with the K and those from the explicit consideration of the {pi}{Sigma}N channel in the three-body equations.« less

Consequences of phosphorus interactions with calcium, phytase, and cholecalciferol on zootechnical performance and mineral retention in broiler chickens.

PubMed

Delezie, E; Maertens, L; Huyghebaert, G

2012-10-01

The objective was to determine the effect of calcium (Ca), total phosphorus (Ptot), cholecalciferol, and phytase level in the diet on the performance, tibia ash percentage, and Ca and P retention in broilers until slaughter age. Broilers were randomly assigned to 12 treatments, each with 6 replicates, comprising 3 diets differing in Ca and P level: 1) normal Ca and Ptot level (NN); 2) normal Ca and low Ptot level (NL), 3) low Ca and Ptot level (LL). Broilers were also given 2 levels of cholecalciferol and 2 levels of phytase. The normal levels of Ca and Ptot for the starter, grower, and finisher phases were 0.90, 0.82, 0.74% and 0.67, 0.62, 0.57%, respectively. The low Ca and Ptot levels for the 3 phases were 0.67, 0.60, 0.52% and 0.57, 0.51, 0.46%, respectively. Broilers of the NL treatment obtained the lowest BW, whereas BW of the NN and LL groups were comparable. Cholecalciferol significantly affected the BW, with differences up to 2.6 and 1.2% for the starter and grower phases, respectively. The highest cholecalciferol effect was found in combination with the NN treatment. The percentage of retained Ca increased from 33% to 41% and 48% when the imbalanced diet was replaced by the NN and LL balanced diets, respectively. P release from phytate was 64 and 67% for the NL and LL diets, respectively. Phytase and cholecalciferol had significantly favorable effects on retention values but these effects were dependent on Ca and Ptot levels and their ratio. In conclusion, both diets with the balanced Ca/Ptot ratio resulted in the best performance, highest tibia ash percentage and P release from phytate. A reduction of the Aviagen (2009) recommended P requirements by 25 to 30% and Ca by 15 to 20% over the various phases did not negatively affect performance, bone development, and improved Ca and Ptot retention. The effects of supplementing cholecalciferol and phytase were additive but not significant and no synergism between both was present.

Influences of the third and fourth nearest neighbouring interactions on the surface anisotropy of face-centred-cubic metals

NASA Astrophysics Data System (ADS)

Luo, Yongkun; Qin, Rongshan

2014-06-01

The structure and the anisotropic properties of the surfaces of face-centred-cubic (FCC) metals have been studied using the broken-bond model while considering the third and fourth nearest neighbouring (3rd and 4th NN) interactions. The pair potential expressions are obtained using the Rose-Vinet universal potential equation. The model is suitable for calculation of the property of a surface with arbitrary crystallographic orientations and can provide absolute unrelaxed surface energy values using three input parameters, namely the lattice constant, bulk modulus and cohesive energy. These parameters are available for the majority of FCC metals. The numerical results for 7 FCC metals have been obtained and compared with these obtained from ab initio calculations and experimental measurements. Good agreement is observed between the two. Taking into account up to the 4th NN interactions, the overall surface energy anisotropy for FCC metals was found to be between 12% to 16%, and the ratio between the surface energies at (100) and (111) planes was found to be 1.05. These values are less than those reported by conventional calculations but more similar to experimental measurements. It is found that the strength of 3rd and 4th NN interactions differs from one element to another, the Ni and Cu interactions being the most significant while the Au, Pt and Pb interactions are the least significant. This suggests that the polar diagrams of the surface energy of Ni and Cu are different from those of Au, Pt and Pb by showing cusps of the unconventional {110} and high-index {210}, {311} and possibly {135} poles. This provides explanations to the recent experimental observations of the {110}, {210}, {311} and {135} facets in equilibrated Ni and Cu crystallines.

Influence of the halothane gene (HAL) on pork quality in two commercial crossbreeds.

PubMed

Silveira, A C P; Freitas, P F A; César, A S M; Cesar, A S M; Antunes, R C; Guimarães, E C; Batista, D F A; Torido, L C

2011-01-01

We evaluated the effect of the halothane (HAL) gene on the quality of pork in domestic pigs. Half-carcasses from two different commercial pig (Sus domestica) crossbreeds were analyzed, 46 of which were homozygous dominant (HAL(NN)) and 69 of which were heterozygous (HAL(Nn)) for the halothane gene. The measures included backfat thickness, lean meat percentage, carcass weight, pH 24 h after slaughtering, color, and drip loss; DNA was extracted from the haunch muscle. Swine with the HAL(Nn) genotype had less backfat thickness and higher lean meat percentages than swine with the HAL(NN) genotype. Yet, swine with the HAL(Nn) genotype had lower quality meat than those with the HAL(NN) swine. The pH at 24 h was lower in HAL(Nn) swine. The meat color was paler in HAL(Nn) animals, the drip loss was greater in those animals bearing the n allele, and the amount of intramuscular fat was not related to the halothane genotype. We conclude that bearers of the recessive allele of the halothane gene produce more meat, but with quality parameters that are inferior to those sought by consumers and industry.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Coarse graining of NN inelastic interactions up to 3 GeV: Repulsive versus structural core

NASA Astrophysics Data System (ADS)

Fernández-Soler, P.; Ruiz Arriola, E.

2017-07-01

The repulsive short-distance core is one of the main paradigms of nuclear physics which even seems confirmed by QCD lattice calculations. On the other hand nuclear potentials at short distances are motivated by high energy behavior where inelasticities play an important role. We analyze NN interactions up to 3 GeV in terms of simple coarse grained complex and energy dependent interactions. We discuss two possible and conflicting scenarios which share the common feature of a vanishing wave function at the core location in the particular case of S waves. We find that the optical potential with a repulsive core exhibits a strong energy dependence whereas the optical potential with the structural core is characterized by a rather adiabatic energy dependence which allows one to treat inelasticity perturbatively. We discuss the possible implications for nuclear structure calculations of both alternatives.

Hartree-Fock studies of hypernuclear properties

NASA Astrophysics Data System (ADS)

Lanskoy, D. E.

1998-08-01

The Skyrme-Hartree-Fock approach is approved as a powerful tool to reproduce general properties of Λ hypernuclear spectra [1-4] and to relate hypernuclear observables to effective interaction features. In this contribution, we consider briefly some less common hypernuclear systems, which appear to be quite sensitive to details of the relevant interactions. Particularly, we address possible manifestations of the polarization of a hypernuclear core (i.e. core distortion due to hyperon addition), which is driven in terms of the Skyrme force mainly by counterbalance between the two-body ΛN force and the three-body ΛNN (or density-dependent ΛN) one.

Full-potential KKR calculations for vacancies in Al : Screening effect and many-body interactions

NASA Astrophysics Data System (ADS)

Hoshino, T.; Asato, M.; Zeller, R.; Dederichs, P. H.

2004-09-01

We give ab initio calculations for vacancies in Al . The calculations are based on the generalized-gradient approximation in the density-functional theory and employ the all-electron full-potential Korringa-Kohn-Rostoker Green’s function method for point defects, which guarantees the correct embedding of the cluster of point defects in an otherwise perfect crystal. First, we confirm the recent calculated results of Carling [Phys. Rev. Lett. 85, 3862 (2000)], i.e., repulsion of the first-nearest-neighbor (1NN) divacancy in Al , and elucidate quantitatively the micromechanism of repulsion. Using the calculated results for vacancy formation energies and divacancy binding energies in Na , Mg , Al , and Si of face-centered-cubic, we show that the single vacancy in nearly free-electron systems becomes very stable with increasing free-electron density, due to the screening effect, and that the formation of divacancy destroys the stable electron distribution around the single vacancy, resulting in a repulsion of two vacancies on 1NN sites, so that the 1NN divacancy is unstable. Second, we show that the cluster expansion converges rapidly for the binding energies of vacancy agglomerates in Al . The binding energy of 13 vacancies consisting of a central vacancy and its 12 nearest neighbors, is reproduced within the error of 0.002eV per vacancy, if many-body interaction energies up to the four-body terms are taken into account in the cluster expansion, being compared with the average error (>0.1eV) of the glue models which are very often used to provide interatomic potentials for computer simulations. For the cluster expansion of the binding energies of impurities, we get the same convergence as that obtained for vacancies. Thus, the present cluster-expansion approach for the binding energies of agglomerates of vacancies and impurities in Al may provide accurate data to construct the interaction-parameter model for computer simulations which are strongly requested to study the dynamical process in the initial stage of the formation of the so-called Guinier-Preston zones of low-concentrated Al -based alloys such as Al1-cXc ( X=Cu , Zn ; c<0.05 ).

Triple-parton scatterings in proton-nucleus collisions at high energies

NASA Astrophysics Data System (ADS)

d'Enterria, David; Snigirev, Alexander M.

2018-05-01

A generic expression to compute triple-parton scattering (TPS) cross sections in high-energy proton-nucleus (pA) collisions is derived as a function of the corresponding single-parton cross sections and an effective parameter encoding the transverse parton profile of the proton. The TPS cross sections are enhanced by a factor of about 9 A˜eq 2000 in pPb as compared to those in proton-nucleon collisions at the same center-of-mass energy. Estimates for triple charm (c\\overline{c}) and bottom (b\\overline{b}) production in pPb collisions at LHC and FCC energies are presented based on next-to-next-to-leading-order calculations for c\\overline{c} and b\\overline{b} single-parton cross sections. At √{s_{_{sc {nn}}}}= 8.8 TeV, about 10% of the pPb events have three c\\overline{c} pairs produced in separate partonic interactions. At √{s_{_{sc {nn}}}}= 63 TeV, the pPb cross sections for triple-J/ψ and triple-b\\overline{b} are O(1-10 mb). In the most energetic collisions of cosmic rays in the upper atmosphere, equivalent to √{s_{_{sc {nn}}}}≈ 400 TeV, the TPS c\\overline{c} cross section equals the total p-Air inelastic cross section.

Breathing Pattern Interpretation as an Alternative and Effective Voice Communication Solution.

PubMed

Elsahar, Yasmin; Bouazza-Marouf, Kaddour; Kerr, David; Gaur, Atul; Kaushik, Vipul; Hu, Sijung

2018-05-15

Augmentative and alternative communication (AAC) systems tend to rely on the interpretation of purposeful gestures for interaction. Existing AAC methods could be cumbersome and limit the solutions in terms of versatility. The study aims to interpret breathing patterns (BPs) to converse with the outside world by means of a unidirectional microphone and researches breathing-pattern interpretation (BPI) to encode messages in an interactive manner with minimal training. We present BP processing work with (1) output synthesized machine-spoken words (SMSW) along with single-channel Weiner filtering (WF) for signal de-noising, and (2) k -nearest neighbor ( k-NN ) classification of BPs associated with embedded dynamic time warping (DTW). An approved protocol to collect analogue modulated BP sets belonging to 4 distinct classes with 10 training BPs per class and 5 live BPs per class was implemented with 23 healthy subjects. An 86% accuracy of k-NN classification was obtained with decreasing error rates of 17%, 14%, and 11% for the live classifications of classes 2, 3, and 4, respectively. The results express a systematic reliability of 89% with increased familiarity. The outcomes from the current AAC setup recommend a durable engineering solution directly beneficial to the sufferers.

Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Li, Deng; Lu, Xiaoming; Cheng, Xinyi; Wang, Liwei

2014-10-01

Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance γ interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 ±0.17 mm and 1.92 ±0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.

Detection of back-to-back proton pairs in charged-current neutrino interactions with the ArgoNeuT detector in the NuMI low energy beam line

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

Acciarri, R.; Adams, C.; Asaadi, J.

2014-07-01

Short range nucleon-nucleon correlations in nuclei (NN SRC) carry important information on nuclear structure and dynamics. NN SRC have been extensively probed through two-nucleon knock- out reactions in both pion and electron scattering experiments. We report here on the detection of two-nucleon knock-out events from neutrino interactions and discuss their topological features as possibly involving NN SRC content in the target argon nuclei. The ArgoNeuT detector in the Main Injector neutrino beam at Fermilab has recorded a sample of 30 fully reconstructed charged current events where the leading muon is accompanied by a pair of protons at the interaction vertex,more » 19 of which have both protons above the Fermi momentum of the Ar nucleus. Out of these 19 events, four are found with the two protons in a strictly back-to-back high momenta configuration directly observed in the final state and can be associated to nucleon Resonance pionless mechanisms involving a pre-existing short range correlated np pair in the nucleus. Another fraction (four events) of the remaining 15 events have a reconstructed back-to-back configuration of a np pair in the initial state, a signature compatible with one-body Quasi Elastic interaction on a neutron in a SRC pair. The detection of these two subsamples of the collected (mu- + 2p) events suggests that mechanisms directly involving nucleon-nucleon SRC pairs in the nucleus are active and can be efficiently explored in neutrino-argon interactions with the LAr TPC technology.« less

A silk peptide fraction restores cognitive function in AF64A-induced Alzheimer disease model rats by increasing expression of choline acetyltransferase gene.

PubMed

Cha, Yeseul; Lee, Sang Hoon; Jang, Su Kil; Guo, Haiyu; Ban, Young-Hwan; Park, Dongsun; Jang, Gwi Yeong; Yeon, Sungho; Lee, Jeong-Yong; Choi, Ehn-Kyoung; Joo, Seong Soo; Jeong, Heon-Sang; Kim, Yun-Bae

2017-01-01

This study investigated the effects of a silk peptide fraction obtained by incubating silk proteins with Protease N and Neutrase (SP-NN) on cognitive dysfunction of Alzheimer disease model rats. In order to elucidate underlying mechanisms, the effect of SP-NN on the expression of choline acetyltransferase (ChAT) mRNA was assessed in F3.ChAT neural stem cells and Neuro2a neuroblastoma cells; active amino acid sequence was identified using HPLC-MS. The expression of ChAT mRNA in F3.ChAT cells increased by 3.79-fold of the control level by treatment with SP-NN fraction. The active peptide in SP-NN was identified as tyrosine-glycine with 238.1 of molecular weight. Male rats were orally administered with SP-NN (50 or 300mg/kg) and challenged with a cholinotoxin AF64A. As a result of brain injury and decreased brain acetylcholine level, AF64A induced astrocytic activation, resulting in impairment of learning and memory function. Treatment with SP-NN exerted recovering activities on acetylcholine depletion and brain injury, as well as cognitive deficit induced by AF64A. The results indicate that, in addition to a neuroprotective activity, the SP-NN preparation restores cognitive function of Alzheimer disease model rats by increasing the release of acetylcholine. Copyright © 2016 Elsevier Inc. All rights reserved.

Pro-neurotensin/neuromedin N expression and processing in human colon cancer cell lines.

PubMed

Rovère, C; Barbero, P; Maoret, J J; Laburthe, M; Kitabgi, P

1998-05-08

The regulatory peptide neurotensin NT has been proposed to exert an autocrine trophic effect on human colon cancers. In the present study, pro-neurotensin/neuromedin N (proNT/NN) expression and processing were investigated in 13 human colon cancer cell lines using a combination of radioimmunoassay and HPLC techniques. All 13 cell lines displayed low to moderate levels of proNT/NN ranging from 10 to 250 fmol/mg protein. However, only 6 (HCT8, LoVo, HT29, C119A, LS174T, and coloDM320) processed the precursor. Three of the latter (HCT8, LS174T, and coloDM320) were analysed in detail with regard to proNT/NN processing pattern and were found to produce NT and large precursor fragments ending with the NT or NN sequence. They had no detectable level of NN. Such a processing pattern resembles that generated by the prohormone convertase PC5. Northern and Western blot analysis of prohormone convertase expression in the 3 cell lines revealed that they were devoid of PC1 and PC2, whereas they all expressed PC5. These data indicate that proNT/NN is a good marker of human colon cancer cell lines while NT is found in only about half of the cell lines. They also suggest that, in addition to NT, several proNT/NN-derived products, possibly generated by PC5, might exert an autocrine positive effect on human colon cancer growth.

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

Ramdasi, O. A.; Kolekar, Y. D.; Kambale, R. C., E-mail: rckambale@gmail.com

The plate-like NaNbO{sub 3} (NN) templates with (100) preferential orientation was synthesized from bismuth layer structured ferroelectric Bi{sub 2.5}Na{sub 3.5}Nb{sub 5}O{sub 18} (BNN) precursor by topochemical microcrystal conversion (TMC) method. The large platelets of BNN were first obtained by molten salt synthesis at the 1125 °C with a salt-to oxide weight ratio 1.5: 1. The anisotropic NN templates were derived from BNN at the 975 °C with BNN/ Na{sub 2}CO{sub 3} molar ratio of 1:1.5. The NaNbO{sub 3} templates have an average length of ~ 10-14 µm. The NN templates retains their elemental constitutes of Na, Nb and O inmore » stoichiometric proportion. The effect of ultrasonication on the orientation factor (F{sub h00}) of NN templates was understood by X-ray diffraction (XRD) and scanning electron microscopy (SEM) results. The degree of (100) orientation of as synthesized NN templates (~57%) was found to be increased (~89%) after ultrasonication. Moreover, the microstructure i.e. alignment / shape of as synthesized NN templates was changed from rectangular (110) orientation to square (100) orientation geometry after ultrasonication. Hence, ultrasonication is a cost effective approach to preparing the textured piezoelectric ceramics by the template grain growth technique using tape casting.« less

Synthesis mechanism and improved (100) oriented NaNbO3 templates by ultrasonication

NASA Astrophysics Data System (ADS)

Ramdasi, O. A.; Kolekar, Y. D.; Kim, D. J.; Song, T. K.; Kambale, R. C.

2016-05-01

The plate-like NaNbO3 (NN) templates with (100) preferential orientation was synthesized from bismuth layer structured ferroelectric Bi2.5Na3.5Nb5O18 (BNN) precursor by topochemical microcrystal conversion (TMC) method. The large platelets of BNN were first obtained by molten salt synthesis at the 1125 °C with a salt-to oxide weight ratio 1.5: 1. The anisotropic NN templates were derived from BNN at the 975 °C with BNN/ Na2CO3 molar ratio of 1:1.5. The NaNbO3 templates have an average length of ~ 10-14 µm. The NN templates retains their elemental constitutes of Na, Nb and O in stoichiometric proportion. The effect of ultrasonication on the orientation factor (Fh00) of NN templates was understood by X-ray diffraction (XRD) and scanning electron microscopy (SEM) results. The degree of (100) orientation of as synthesized NN templates (~57%) was found to be increased (~89%) after ultrasonication. Moreover, the microstructure i.e. alignment / shape of as synthesized NN templates was changed from rectangular (110) orientation to square (100) orientation geometry after ultrasonication. Hence, ultrasonication is a cost effective approach to preparing the textured piezoelectric ceramics by the template grain growth technique using tape casting.

Effects of ice massage of the head and spine on heart rate variability in healthy volunteers.

PubMed

Mooventhan, A; Nivethitha, L

2016-07-01

Ice massage (IM) is one of the treatment procedures used in hydrotherapy. Though its various physiological/therapeutic effects have been reported, effects of IM of the head and spine on heart rate variability (HRV) have not been studied. Thus, this study evaluated the effects of IM of the head and spine on HRV in healthy volunteers. Thirty subjects were randomly divided into 3 sessions: (1) IM, (2) tap water massage (TWM) and (3) prone rest (PR). Heart rate (HR) and HRV were assessed before and after each intervention session. A significant increase in the mean of the intervals between adjacent QRS complexes or the instantaneous HR (RRI), square root of mean of sum of squares of differences between adjacent normal to normal (NN) intervals (RMSSD), number of interval differences of successive NN intervals greater than 50 milliseconds (NN50), proportion derived by dividing NN50 by total number of NN intervals along with significant reduction in HR after IM session; significant increase in RRI along with significant reduction in HR after TWM, and a significant increase only in RMSSD after PR were observed. However, there was no significant difference between the sessions. Results of this study suggest that 20 min of IM of the head and spine is effective in reducing HR and improving HRV through vagal dominance in healthy volunteers.

Polycyclic Aromatic Hydrocarbons-Associated MicroRNAs and Heart Rate Variability in Coke Oven Workers.

PubMed

Huang, Suli; Deng, Qifei; Feng, Jing; Zhang, Xiaomin; Dai, Xiayun; Li, Lu; Yang, Binyao; Wu, Tangchun; Cheng, Jinquan

2016-01-01

We aimed to evaluate the association between polycyclic aromatic hydrocarbons (PAHs)-related microRNAs (miRNAs) and heart rate variability indices in coke oven workers. We recruited 365 male coke oven workers and measured urinary PAH metabolites by gas chromatography-mass spectrometry. Five heart rate variability indices were measured using three-channel Holter monitor. Six miRNAs were detected by TaqMan miRNA assays (Life Technologies, Foster City, CA). miR-24-3p, miR-27a-3p, miR-142-5p, and miR-320b were negatively associated with the root mean of square of successive differences between adjacent normal NN intervals (RMSSD) (P(trend) = 0.006, 0.047, 0.019, 0.011, respectively). miR-142-5p and miR-320b were also negatively associated with standard deviation of all normal to normal NN intervals (SDNN) (P(trend) = 0.01 and 0.035). miR-24-3p, miR-27a-3p, and miR-320b were significantly interacted with multiple PAH metabolites and influenced heart rate variability indices, whereas miR-24-3p also significantly interacted with smoking to influence low frequency (P(interaction) < 0.05 for all). Plasma miRNAs might act as potential biomarkers for the adverse effect of PAH exposure on the cardiovascular system.

Study of p-4He total reaction cross-section using Glauber and Coulomb-modified Glauber models

NASA Astrophysics Data System (ADS)

Tag El-Din, Ibrahim M. A.; Taha, M. M.; Hassan, Samia S. A.

2014-02-01

The total nuclear reaction cross-section σR for p-4He in the energy range from 25 MeV to 1000 MeV is calculated within Glauber and Coulomb-modified Glauber models. The Coulomb-modified Glauber model (CMGM) is introduced via modification of the Coulomb trajectory of the projectile from a straight line, and calculation of the effective radius of interaction. The effects of in-medium nucleon-nucleon (NN) total cross-section, phase variation, high order momentum transfer component of nucleon-nucleon elastic scattering amplitude and Pauli blocking are studied. It is pointed out that the phase variation of the nucleon-nucleon amplitude plays a significant role in describing σR with γ = -1.6 fm2 at in-medium nuclear density ϱ = 0 and γ = -2 fm2 at ϱ = 0.17 fm-3 in the whole energy range. A remarkable fit to the available experimental data is obtained by invoking Pauli blocking and high order momentum transfer of nucleon-nucleon (NN) elastic scattering amplitude for Ep < 100 MeV.

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

Konobeevski, E. S., E-mail: konobeev@inr.ru; Burmistrov, Yu. M.; Zuyev, S. V.

The first results are obtained in a kinematically complete experiment devoted to measuring the n + d {sup {yields}}p + n + n reaction yield at energies in the range E{sub n} = 40-60 MeV and various angles of divergence of two neutrons ({Delta}{theta} = 4{sup o}, 6{sup o}, and 8{sup o}) in the geometry of neutron-neutron final-state interaction. The {sup 1}S{sub 0} neutron-neutron scattering length a{sub nn} is determined by comparing the experimental energy dependence of the reaction yield with the results of a simulation in the Watson-Migdal approximation, which depend on a{sub nn}. For E{sub n} = 40more » MeV and {Delta}{theta} = 6{sup o} (the best statistics in the experiment), the value a{sub nn} = -17.9 {+-} 1.0 fm was obtained. A further improvement of the experimental accuracy will make it possible to remove the existing disagreement of the results from different experiments.« less

Open charm yields in d+Au collisions at squareroot[sNN]=200 GeV.

PubMed

Adams, J; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Arkhipkin, D; Averichev, G S; Badyal, S K; Bai, Y; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellwied, R; Berger, J; Bezverkhny, B I; Bharadwaj, S; Bhasin, A; Bhati, A K; Bhatia, V S; Bichsel, H; Billmeier, A; Bland, L C; Blyth, C O; Bonner, B E; Botje, M; Boucham, A; Brandin, A V; Bravar, A; Bystersky, M; Cadman, R V; Cai, X Z; Caines, H; Calderón de la Barca Sánchez, M; Castillo, J; Cebra, D; Chajecki, Z; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, Y; Cheng, J; Cherney, M; Chikanian, A; Christie, W; Coffin, J P; Cormier, T M; Cramer, J G; Crawford, H J; Das, D; Das, S; de Moura, M M; Derevschikov, A A; Didenko, L; Dietel, T; Dogra, S M; Dong, W J; Dong, X; Draper, J E; Du, F; Dubey, A K; Dunin, V B; Dunlop, J C; Dutta Mazumdar, M R; Eckardt, V; Edwards, W R; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Faivre, J; Fatemi, R; Fedorisin, J; Filimonov, K; Filip, P; Finch, E; Fine, V; Fisyak, Y; Fomenko, K; Fu, J; Gagliardi, C A; Gaillard, L; Gans, J; Ganti, M S; Gaudichet, L; Geurts, F; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Grachov, O; Grebenyuk, O; Grosnick, D; Guertin, S M; Guo, Y; Gupta, A; Gutierrez, T D; Hallman, T J; Hamed, A; Hardtke, D; Harris, J W; Heinz, M; Henry, T W; Hepplemann, S; Hippolyte, B; Hirsch, A; Hjort, E; Hoffmann, G W; Huang, H Z; Huang, S L; Hughes, E W; Humanic, T J; Igo, G; Ishihara, A; Jacobs, P; Jacobs, W W; Janik, M; Jiang, H; Jones, P G; Judd, E G; Kabana, S; Kang, K; Kaplan, M; Keane, D; Khodyrev, V Yu; Kiryluk, J; Kisiel, A; Kislov, E M; Klay, J; Klein, S R; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kutuev, R Kh; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; Laue, F; Lauret, J; Lebedev, A; Lednicky, R; Lehocka, S; LeVine, M J; Li, C; Li, Q; Li, Y; Lin, G; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, L; Liu, Q J; Liu, Z; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Lopez-Noriega, M; Love, W A; Lu, Y; Ludlam, T; Lynn, D; Ma, G L; Ma, J G; Ma, Y G; Magestro, D; Mahajan, S; Mahapatra, D P; Majka, R; Mangotra, L K; Manweiler, R; Margetis, S; Markert, C; Martin, L; Marx, J N; Matis, H S; Matulenko, Yu A; McClain, C J; McShane, T S; Meissner, F; Melnick, Yu; Meschanin, A; Miller, M L; Minaev, N G; Mironov, C; Mischke, A; Mishra, D K; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Morozov, D A; Munhoz, M G; Nandi, B K; Nayak, S K; Nayak, T K; Nelson, J M; Netrakanti, P K; Nikitin, V A; Nogach, L V; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Peitzmann, T; Perevoztchikov, V; Perkins, C; Peryt, W; Petrov, V A; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rakness, G; Raniwala, R; Raniwala, S; Ravel, O; Ray, R L; Razin, S V; Reichhold, D; Reid, J G; Renault, G; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevskiy, O V; Romero, J L; Rose, A; Roy, C; Ruan, L; Sahoo, R; Sakrejda, I; Salur, S; Sandweiss, J; Sarsour, M; Savin, I; Sazhin, P S; Schambach, J; Scharenberg, R P; Schmitz, N; Schweda, K; Seger, J; Seyboth, P; Shahaliev, E; Shao, M; Shao, W; Sharma, M; Shen, W Q; Shestermanov, K E; Shimanskiy, S S; Sichtermann, E; Simon, F; Singaraju, R N; Skoro, G; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Speltz, J; Spinka, H M; Srivastava, B; Stadnik, A; Stanislaus, T D S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Suaide, A A P; Sugarbaker, E; Suire, C; Sumbera, M; Surrow, B; Symons, T J M; Szanto de Toledo, A; Szarwas, P; Tai, A; Takahashi, J; Tang, A H; Tarnowsky, T; Thein, D; Thomas, J H; Timoshenko, S; Tokarev, M; Trainor, T A; Trentalange, S; Tribble, R E; Tsai, O D; Ulery, J; Ullrich, T; Underwood, D G; Urkinbaev, A; Van Buren, G; van Leeuwen, M; Vander Molen, A M; Varma, R; Vasilevski, I M; Vasiliev, A N; Vernet, R; Vigdor, S E; Viyogi, Y P; Vokal, S; Voloshin, S A; Vznuzdaev, M; Waggoner, W T; Wang, F; Wang, G; Wang, G; Wang, X L; Wang, Y; Wang, Y; Wang, Z M; Ward, H; Watson, J W; Webb, J C; Wells, R; Westfall, G D; Wetzler, A; Whitten, C; Wieman, H; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Z; Xu, Z Z; Yamamoto, E; Yepes, P; Yurevich, V I; Zanevsky, Y V; Zhang, H; Zhang, W M; Zhang, Z P; Zoulkarneev, R; Zoulkarneeva, Y; Zubarev, A N

2005-02-18

Midrapidity open charm spectra from direct reconstruction of D0(D0)-->K-/+pi+/- in d+Au collisions and indirect electron-positron measurements via charm semileptonic decays in p+p and d+Au collisions at squareroot[sNN]=200 GeV are reported. The D0(D0) spectrum covers a transverse momentum (pT) range of 0.1

A comparison of human performance in figural and navigational versions of the traveling salesman problem.

PubMed

Blaser, R E; Wilber, Julie

2013-11-01

Performance on a typical pen-and-paper (figural) version of the Traveling Salesman Problem was compared to performance on a room-sized navigational version of the same task. Nine configurations were designed to examine the use of the nearest-neighbor (NN), cluster approach, and convex-hull strategies. Performance decreased with an increasing number of nodes internal to the hull, and improved when the NN strategy produced the optimal path. There was no overall difference in performance between figural and navigational task modalities. However, there was an interaction between modality and configuration, with evidence that participants relied more heavily on the NN strategy in the figural condition. Our results suggest that participants employed similar, but not identical, strategies when solving figural and navigational versions of the problem. Surprisingly, there was no evidence that participants favored global strategies in the figural version and local strategies in the navigational version.

Large-basis ab initio no-core shell model and its application to {sup 12}C

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

Navratil, P.; Vary, J. P.; Barrett, B. R.

2000-11-01

We present the framework for the ab initio no-core nuclear shell model and apply it to obtain properties of {sup 12}C. We derive two-body effective interactions microscopically for specific model spaces from the realistic CD-Bonn and the Argonne V8' nucleon-nucleon (NN) potentials. We then evaluate binding energies, excitation spectra, radii, and electromagnetic transitions in the 0{Dirac_h}{Omega}, 2{Dirac_h}{Omega}, and 4{Dirac_h}{Omega} model spaces for the positive-parity states and the 1{Dirac_h}{Omega}, 3{Dirac_h}{Omega}, and 5{Dirac_h}{Omega} model spaces for the negative-parity states. Dependence on the model-space size, on the harmonic-oscillator frequency, and on the type of the NN potential, used for the effective interaction derivation,more » are studied. In addition, electromagnetic and weak neutral elastic charge form factors are calculated in the impulse approximation. Sensitivity of the form-factor ratios to the strangeness one-body form-factor parameters and to the influence of isospin-symmetry violation is evaluated and discussed. Agreement between theory and experiment is favorable for many observables, while others require yet larger model spaces and/or three-body forces. The limitations of the present results are easily understood by virtue of the trends established and previous phenomenological results.« less

Alternate Implosion Models for the Plane Parallel Diode.

DTIC Science & Technology

1982-04-02

higher density regions, perhaps altering the spectral mix of line and continuum radiation. 3) Examine the effect of the chemical potential (properly...inclusion of previous time levels (i-i, i-2...) in an estimate of AF would be a means of filtering the noise that might develop in this variable, and...NN’N NO G~l-0NNV . OO’)N aQ.’)4Ŕ N 4 . * .~ 0 I’) MMIs )NN4 P).N 44 NN 4 0. 00(0 0-0 NN 1 OWEI) ~4N Vi N 0 vm -N 0. 0N0 M a 40 0 I.) 0.0. N Nflmwo ONNOM

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Wide field imaging - I. Applications of neural networks to object detection and star/galaxy classification

NASA Astrophysics Data System (ADS)

Andreon, S.; Gargiulo, G.; Longo, G.; Tagliaferri, R.; Capuano, N.

2000-12-01

Astronomical wide-field imaging performed with new large-format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of `what an object is' (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features we use a NN to select the most significant features among the large number of measured ones, and then we use these selected features to perform the classification task. In order to optimize the performance of the system, we implemented and tested several different models of NN. The comparison of the NExt performance with that of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.

Cold-nuclear-matter effects on heavy-quark production at forward and backward rapidity in d + Au collisions at √sNN = 200  GeV.

PubMed

Adare, A; Aidala, C; Ajitanand, N N; Akiba, Y; Akimoto, R; Al-Bataineh, H; Al-Ta'ani, H; Alexander, J; Andrews, K R; Angerami, A; Aoki, K; Apadula, N; Appelt, E; Aramaki, Y; Armendariz, R; Aschenauer, E C; Atomssa, E T; Averbeck, R; Awes, T C; Azmoun, B; Babintsev, V; Bai, M; Baksay, G; Baksay, L; Bannier, B; Barish, K N; Bassalleck, B; Basye, A T; Bathe, S; Baublis, V; Baumann, C; Bazilevsky, A; Belikov, S; Belmont, R; Ben-Benjamin, J; Bennett, R; Bhom, J H; Blau, D S; Bok, J S; Boyle, K; Brooks, M L; Broxmeyer, D; Buesching, H; Bumazhnov, V; Bunce, G; Butsyk, S; Campbell, S; Caringi, A; Castera, P; Chen, C-H; Chi, C Y; Chiu, M; Choi, I J; Choi, J B; Choudhury, R K; Christiansen, P; Chujo, T; Chung, P; Chvala, O; Cianciolo, V; Citron, Z; Cole, B A; Conesa Del Valle, Z; Connors, M; Csanád, M; Csörgő, T; Dahms, T; Dairaku, S; Danchev, I; Das, K; Datta, A; David, G; Dayananda, M K; Denisov, A; Deshpande, A; Desmond, E J; Dharmawardane, K V; Dietzsch, O; Dion, A; Donadelli, M; Drapier, O; Drees, A; Drees, K A; Durham, J M; Durum, A; Dutta, D; D'Orazio, L; Edwards, S; Efremenko, Y V; Ellinghaus, F; Engelmore, T; Enokizono, A; En'yo, H; Esumi, S; Fadem, B; Fields, D E; Finger, M; Finger, M; Fleuret, F; Fokin, S L; Fraenkel, Z; Frantz, J E; Franz, A; Frawley, A D; Fujiwara, K; Fukao, Y; Fusayasu, T; Gal, C; Garishvili, I; Glenn, A; Gong, H; Gong, X; Gonin, M; Goto, Y; Granier de Cassagnac, R; Grau, N; Greene, S V; Grim, G; Grosse Perdekamp, M; Gunji, T; Guo, L; Gustafsson, H-Å; Haggerty, J S; Hahn, K I; Hamagaki, H; Hamblen, J; Han, R; Hanks, J; Harper, C; Hashimoto, K; Haslum, E; Hayano, R; He, X; Heffner, M; Hemmick, T K; Hester, T; Hill, J C; Hohlmann, M; Hollis, R S; Holzmann, W; Homma, K; Hong, B; Horaguchi, T; Hori, Y; Hornback, D; Huang, S; Ichihara, T; Ichimiya, R; Iinuma, H; Ikeda, Y; Imai, K; Inaba, M; Iordanova, A; Isenhower, D; Ishihara, M; Issah, M; Ivanischev, D; Iwanaga, Y; Jacak, B V; Jia, J; Jiang, X; Jin, J; John, D; Johnson, B M; Jones, T; Joo, K S; Jouan, D; Jumper, D S; Kajihara, F; Kamin, J; Kaneti, S; Kang, B H; Kang, J H; Kang, J S; Kapustinsky, J; Karatsu, K; Kasai, M; Kawall, D; Kawashima, M; Kazantsev, A V; Kempel, T; Khanzadeev, A; Kijima, K M; Kikuchi, J; Kim, A; Kim, B I; Kim, D J; Kim, E-J; Kim, Y-J; Kim, Y K; Kinney, E; Kiss, A; Kistenev, E; Kleinjan, D; Kline, P; Kochenda, L; Komkov, B; Konno, M; Koster, J; Kotov, D; Král, A; Kravitz, A; Kunde, G J; Kurita, K; Kurosawa, M; Kwon, Y; Kyle, G S; Lacey, R; Lai, Y S; Lajoie, J G; Lebedev, A; Lee, D M; Lee, J; Lee, K B; Lee, K S; Lee, S H; Lee, S R; Leitch, M J; Leite, M A L; Li, X; Lichtenwalner, P; Liebing, P; Lim, S H; Linden Levy, L A; Liška, T; Liu, H; Liu, M X; Love, B; Lynch, D; Maguire, C F; Makdisi, Y I; Malik, M D; Manion, A; Manko, V I; Mannel, E; Mao, Y; Masui, H; Matathias, F; McCumber, M; McGaughey, P L; McGlinchey, D; McKinney, C; Means, N; Mendoza, M; Meredith, B; Miake, Y; Mibe, T; Mignerey, A C; Miki, K; Milov, A; Mitchell, J T; Miyachi, Y; Mohanty, A K; Moon, H J; Morino, Y; Morreale, A; Morrison, D P; Motschwiller, S; Moukhanova, T V; Murakami, T; Murata, J; Nagamiya, S; Nagle, J L; Naglis, M; Nagy, M I; Nakagawa, I; Nakamiya, Y; Nakamura, K R; Nakamura, T; Nakano, K; Nam, S; Newby, J; Nguyen, M; Nihashi, M; Nouicer, R; Nyanin, A S; Oakley, C; O'Brien, E; Oda, S X; Ogilvie, C A; Oka, M; Okada, K; Onuki, Y; Oskarsson, A; Ouchida, M; Ozawa, K; Pak, R; Pantuev, V; Papavassiliou, V; Park, B H; Park, I H; Park, S K; Park, W J; Pate, S F; Patel, L; Pei, H; Peng, J-C; Pereira, H; Peressounko, D Yu; Petti, R; Pinkenburg, C; Pisani, R P; Proissl, M; Purschke, M L; Qu, H; Rak, J; Ravinovich, I; Read, K F; Rembeczki, S; Reygers, K; Riabov, V; Riabov, Y; Richardson, E; Roach, D; Roche, G; Rolnick, S D; Rosati, M; Rosen, C A; Rosendahl, S S E; Ružička, P; Sahlmueller, B; Saito, N; Sakaguchi, T; Sakashita, K; Samsonov, V; Sano, S; Sarsour, M; Sato, T; Savastio, M; Sawada, S; Sedgwick, K; Seele, J; Seidl, R; Seto, R; Sharma, D; Shein, I; Shibata, T-A; Shigaki, K; Shim, H H; Shimomura, M; Shoji, K; Shukla, P; Sickles, A; Silva, C L; Silvermyr, D; Silvestre, C; Sim, K S; Singh, B K; Singh, C P; Singh, V; Slunečka, M; Sodre, T; Soltz, R A; Sondheim, W E; Sorensen, S P; Sourikova, I V; Stankus, P W; Stenlund, E; Stoll, S P; Sugitate, T; Sukhanov, A; Sun, J; Sziklai, J; Takagui, E M; Takahara, A; Taketani, A; Tanabe, R; Tanaka, Y; Taneja, S; Tanida, K; Tannenbaum, M J; Tarafdar, S; Taranenko, A; Tennant, E; Themann, H; Thomas, D; Thomas, T L; Togawa, M; Toia, A; Tomášek, L; Tomášek, M; Torii, H; Towell, R S; Tserruya, I; Tsuchimoto, Y; Utsunomiya, K; Vale, C; Valle, H; van Hecke, H W; Vazquez-Zambrano, E; Veicht, A; Velkovska, J; Vértesi, R; Virius, M; Vossen, A; Vrba, V; Vznuzdaev, E; Wang, X R; Watanabe, D; Watanabe, K; Watanabe, Y; Watanabe, Y S; Wei, F; Wei, R; Wessels, J; White, S N; Winter, D; Woody, C L; Wright, R M; Wysocki, M; Yamaguchi, Y L; Yamaura, K; Yang, R; Yanovich, A; Ying, J; Yokkaichi, S; Yoo, J S; You, Z; Young, G R; Younus, I; Yushmanov, I E; Zajc, W A; Zelenski, A; Zhou, S

2014-06-27

The PHENIX experiment has measured open heavy-flavor production via semileptonic decay over the transverse momentum range 1 < p(T) < 6  GeV/c at forward and backward rapidity (1.4 < |y| < 2.0) in d+Au and p + p collisions at √sNN = 200  GeV. In central d+Au collisions, relative to the yield in p + p collisions scaled by the number of binary nucleon-nucleon collisions, a suppression is observed at forward rapidity (in the d-going direction) and an enhancement at backward rapidity (in the Au-going direction). Predictions using nuclear-modified-parton-distribution functions, even with additional nuclear-p(T) broadening, cannot simultaneously reproduce the data at both rapidity ranges, which implies that these models are incomplete and suggests the possible importance of final-state interactions in the asymmetric d + Au collision system. These results can be used to probe cold-nuclear-matter effects, which may significantly affect heavy-quark production, in addition to helping constrain the magnitude of charmonia-breakup effects in nuclear matter.

Effects of NN potentials on p Nuclides in the A ˜100-120 region

NASA Astrophysics Data System (ADS)

Lahiri, C.; Biswal, S. K.; Patra, S. K.

2016-02-01

Microscopic optical potentials for low-energy proton reactions have been obtained by folding density dependent M3Y (DDM3Y) interaction derived from nuclear matter calculation with densities from mean field approach to study astrophysically important proton rich nuclei in mass 100-120 region. We compare S factors for low-energy (p,γ) reactions with available experimental data and further calculate astrophysical reaction rates for (p,γ) and (p,n) reactions. Again, we choose some nonlinear R3Y (NR3Y) interactions from relativistic mean field (RMF) calculation and folded them with corresponding RMF densities to reproduce experimental S-factor values in this mass region. Finally, the effect of nonlinearity on our result is discussed.

Initial eccentricity in deformed {sup 197}Au+{sup 197}Au and {sup 238}U+{sup 238}U collisions at {radical}(s{sub NN})=200 GeV at the BNL Relativistic Heavy Ion Collider

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

Filip, Peter; Lednicky, Richard; Masui, Hiroshi

2009-11-15

Initial eccentricity and eccentricity fluctuations of the interaction volume created in relativistic collisions of deformed {sup 197}Au and {sup 238}U nuclei are studied using optical and Monte Carlo (MC) Glauber simulations. It is found that the nonsphericity noticeably influences the average eccentricity in central collisions, and eccentricity fluctuations are enhanced from deformation. Quantitative results are obtained for Au+Au and U+U collisions at energy {radical}(s{sub NN})=200 GeV.

Peptidase inhibitors potentiate the effects of neurotensin and neuromedin N on self-stimulation of the medial prefrontal cortex.

PubMed

Fernández, R; Alba, F; Ferrer, J M

1996-02-29

The purpose of this study was to examine the possible role of endogenous peptidases in the inhibition of intracranial self-stimulation (ICSS) produced by injections of neurotensin (NT) and neuromedin N (NN) into the medial prefrontal cortex (MPC) of the rat. We studied the effects on ICSS of the MPC of the administration of thiorphan and bestatin, two specific inhibitors of the peptidases that inactivate NT and NN respectively. Microinjections into MPC of thiorphan (10 micrograms) and bestatin (25 micrograms) potentiated in inhibition of ICSS produced by the intracortical administration of NT (10 nmol) and NN (20 nmol) respectively. This potentiation affected both the amplitude and the duration of the inhibition of ICSS produced by the neuropeptides. Our data indicate that endogenous peptidases are involved in the inactivation of NT and NN in the prefrontal cortex.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Supernova Neutrino Opacity from Nucleon-Nucleon Bremsstrahlung and Related Processes

NASA Astrophysics Data System (ADS)

Hannestad, Steen; Raffelt, Georg

1998-11-01

Elastic scattering on nucleons, νN --> Nν, is the dominant supernova (SN) opacity source for μ and τ neutrinos. The dominant energy- and number-changing processes were thought to be νe- --> e-ν and νν¯<-->e+e- until Suzuki showed that the bremsstrahlung process νν¯NN<-->NN was actually more important. We find that for energy exchange, the related ``inelastic scattering process'' νNN<-->NNν is even more effective by about a factor of 10. A simple estimate implies that the νμ and ντ spectra emitted during the Kelvin-Helmholtz cooling phase are much closer to that of ν¯e than had been thought previously. To facilitate a numerical study of the spectra formation we derive a scattering kernel that governs both bremsstrahlung and inelastic scattering and give an analytic approximation formula. We consider only neutron-neutron interactions; we use a one-pion exchange potential in Born approximation, nonrelativistic neutrons, and the long-wavelength limit, simplifications that appear justified for the surface layers of an SN core. We include the pion mass in the potential, and we allow for an arbitrary degree of neutron degeneracy. Our treatment does not include the neutron-proton process and does not include nucleon-nucleon correlations. Our perturbative approach applies only to the SN surface layers, i.e., to densities below about 1014 g cm-3.

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

Zhang Yanxia; Ma He; Peng Nanbo

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISEmore » sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.« less

D -meson production in p -Pb collisions at s NN = 5.02 TeV and in p p collisions at s = 7 TeV

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

Adam, J.; Adamová, D.; Aggarwal, M. M.

Bmore » ackground: In the context of the investigation of the quark gluon plasma produced in heavy-ion collisions, hadrons containing heavy (charm or beauty) quarks play a special role for the characterization of the hot and dense medium created in the interaction. The measurement of the production of charm and beauty hadrons in proton-proton collisions, besides providing the necessary reference for the studies in heavy-ion reactions, constitutes an important test of perturbative quantum chromodynamics (pQCD) calculations. Heavy-flavor production in proton-nucleus collisions is sensitive to the various effects related to the presence of nuclei in the colliding system, commonly denoted cold-nuclear-matter effects. Most of these effects are expected to modify open-charm production at low transverse momenta (pT) and, so far, no measurement of D-meson production down to zero transverse momentum was available at mid-rapidity at the energies attained at the CERN Large Hadron Collider (LHC). Purpose: The measurements of the production cross sections of promptly produced charmed mesons in p-Pb collisions at the LHC down to pT=0 and the comparison to the results from pp interactions are aimed at the assessment of cold-nuclear-matter effects on open-charm production, which is crucial for the interpretation of the results from Pb-Pb collisions. Methods: The prompt charmed mesons D 0,D +,D⁺+, and D S + were measured at mid-rapidity in p-Pb collisions at a center-of-mass energy per nucleon pair s NN =5.02 TeV with the ALICE detector at the LHC. D mesons were reconstructed from their decays D0→K-π+,D+→K-π+π+, D⁺+→D0π+,Ds+→φπ+→K-K+π+, and their charge conjugates, using an analysis method based on the selection of decay topologies displaced from the interaction vertex. In addition, the prompt D0 production cross section was measured in pp collisions at s NN =7 TeV and p-Pb collisions at s NN =5.02 TeV down to pT=0 using an analysis technique that is based on the estimation and subtraction of the combinatorial background, without reconstruction of the D0 decay vertex. Results: The production cross section in pp collisions is described within uncertainties by different implementations of pQCD calculations down to pT=0. This allowed also a determination of the total cc production cross section in pp collisions, which is more precise than previous ALICE measurements because it is not affected by uncertainties owing to the extrapolation to pT=0. The nuclear modification factor RpPb(pT), defined as the ratio of the pT-differential D meson cross section in p-Pb collisions and that in pp collisions scaled by the mass number of the Pb nucleus, was calculated for the four D-meson species and found to be compatible with unity within uncertainties. The results are compared to theoretical calculations that include cold-nuclear-matter effects and to transport model calculations incorporating the interactions of charm quarks with an expanding deconfined medium. Conclusions: These measurements add experimental evidence that the modification of the D-meson transverse momentum distributions observed in Pb-Pb collisions with respect to pp interactions is due to strong final-state effects induced by the interactions of the charm quarks with the hot and dense partonic medium created in ultrarelativistic heavy-ion collisions. The current precision of the measurement does not allow us to draw conclusions on the role of the different cold-nuclear-matter effects and on the possible presence of additional hot-medium effects in p-Pb collisions. However, the analysis technique without decay-vertex reconstruction, applied on future larger data samples, should provide access to the physics-rich range down to pT=0.« less

D -meson production in p -Pb collisions at s NN = 5.02 TeV and in p p collisions at s = 7 TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2016-11-23

Bmore » ackground: In the context of the investigation of the quark gluon plasma produced in heavy-ion collisions, hadrons containing heavy (charm or beauty) quarks play a special role for the characterization of the hot and dense medium created in the interaction. The measurement of the production of charm and beauty hadrons in proton-proton collisions, besides providing the necessary reference for the studies in heavy-ion reactions, constitutes an important test of perturbative quantum chromodynamics (pQCD) calculations. Heavy-flavor production in proton-nucleus collisions is sensitive to the various effects related to the presence of nuclei in the colliding system, commonly denoted cold-nuclear-matter effects. Most of these effects are expected to modify open-charm production at low transverse momenta (pT) and, so far, no measurement of D-meson production down to zero transverse momentum was available at mid-rapidity at the energies attained at the CERN Large Hadron Collider (LHC). Purpose: The measurements of the production cross sections of promptly produced charmed mesons in p-Pb collisions at the LHC down to pT=0 and the comparison to the results from pp interactions are aimed at the assessment of cold-nuclear-matter effects on open-charm production, which is crucial for the interpretation of the results from Pb-Pb collisions. Methods: The prompt charmed mesons D 0,D +,D⁺+, and D S + were measured at mid-rapidity in p-Pb collisions at a center-of-mass energy per nucleon pair s NN =5.02 TeV with the ALICE detector at the LHC. D mesons were reconstructed from their decays D0→K-π+,D+→K-π+π+, D⁺+→D0π+,Ds+→φπ+→K-K+π+, and their charge conjugates, using an analysis method based on the selection of decay topologies displaced from the interaction vertex. In addition, the prompt D0 production cross section was measured in pp collisions at s NN =7 TeV and p-Pb collisions at s NN =5.02 TeV down to pT=0 using an analysis technique that is based on the estimation and subtraction of the combinatorial background, without reconstruction of the D0 decay vertex. Results: The production cross section in pp collisions is described within uncertainties by different implementations of pQCD calculations down to pT=0. This allowed also a determination of the total cc production cross section in pp collisions, which is more precise than previous ALICE measurements because it is not affected by uncertainties owing to the extrapolation to pT=0. The nuclear modification factor RpPb(pT), defined as the ratio of the pT-differential D meson cross section in p-Pb collisions and that in pp collisions scaled by the mass number of the Pb nucleus, was calculated for the four D-meson species and found to be compatible with unity within uncertainties. The results are compared to theoretical calculations that include cold-nuclear-matter effects and to transport model calculations incorporating the interactions of charm quarks with an expanding deconfined medium. Conclusions: These measurements add experimental evidence that the modification of the D-meson transverse momentum distributions observed in Pb-Pb collisions with respect to pp interactions is due to strong final-state effects induced by the interactions of the charm quarks with the hot and dense partonic medium created in ultrarelativistic heavy-ion collisions. The current precision of the measurement does not allow us to draw conclusions on the role of the different cold-nuclear-matter effects and on the possible presence of additional hot-medium effects in p-Pb collisions. However, the analysis technique without decay-vertex reconstruction, applied on future larger data samples, should provide access to the physics-rich range down to pT=0.« less

Studies on the Mechanism of Action of Hydrazine-Induced Methylation of DNA Guanne

DTIC Science & Technology

1984-10-03

potent methylating agent , diazomethane (-CH -N+-N). Several in vivo studies were carried out to determine the role of aldehydes in the alkylation of DNA...methylating agent available to interact with DNA. If such a mechanism occurs, it may explain why disulfiram appears to inhibit the alkylation of DNA...a much slower/poorer alkylating agent for DNA. Effect of the 1-Carbon Pool on DNA Methylation in Hydrazine Toxicity: In Vitro In vitro studies were

Baryon-Baryon Interactions ---Nijmegen Extended-Soft-Core Models---

NASA Astrophysics Data System (ADS)

Rijken, T. A.; Nagels, M. M.; Yamamoto, Y.

We review the Nijmegen extended-soft-core (ESC) models for the baryon-baryon (BB) interactions of the SU(3) flavor-octet of baryons (N, Lambda, Sigma, and Xi). The interactions are basically studied from the meson-exchange point of view, in the spirit of the Yukawa-approach to the nuclear force problem [H. Yukawa, ``On the interaction of Elementary Particles I'', Proceedings of the Physico-Mathematical Society of Japan 17 (1935), 48], using generalized soft-core Yukawa-functions. These interactions are supplemented with (i) multiple-gluon-exchange, and (ii) structural effects due to the quark-core of the baryons. We present in some detail the most recent extended-soft-core model, henceforth referred to as ESC08, which is the most complete, sophisticated, and successful interaction-model. Furthermore, we discuss briefly its predecessor the ESC04-model [Th. A. Rijken and Y. Yamamoto, Phys. Rev. C 73 (2006), 044007; Th. A. Rijken and Y. Yamamoto, Ph ys. Rev. C 73 (2006), 044008; Th. A. Rijken and Y. Yamamoto, nucl-th/0608074]. For the soft-core one-boson-exchange (OBE) models we refer to the literature [Th. A. Rijken, in Proceedings of the International Conference on Few-Body Problems in Nuclear and Particle Physics, Quebec, 1974, ed. R. J. Slobodrian, B. Cuec and R. Ramavataram (Presses Universitè Laval, Quebec, 1975), p. 136; Th. A. Rijken, Ph. D. thesis, University of Nijmegen, 1975; M. M. Nagels, Th. A. Rijken and J. J. de Swart, Phys. Rev. D 17 (1978), 768; P. M. M. Maessen, Th. A. Rijken and J. J. de Swart, Phys. Rev. C 40 (1989), 2226; Th. A. Rijken, V. G. J. Stoks and Y. Yamamoto, Phys. Rev. C 59 (1999), 21; V. G. J. Stoks and Th. A. Rijken, Phys. Rev. C 59 (1999), 3009]. All ingredients of these latter models are also part of ESC08, and so a description of ESC08 comprises all models so far in principle. The extended-soft-core (ESC) interactions consist of local- and non-local-potentials due to (i) one-boson-exchanges (OBE), which are the members of nonets of pseudo-scalar-, vector-, scalar-, and axial-mesons, (ii) diffractive (i.e. multiple-gluon) exchanges, (iii) two pseudo-scalar exchange (PS-PS), and (iv) meson-pair-exchange (MPE). The OBE- and pair-vertices are regulated by gaussian form factors producing potentials with a soft behavior near the origin. The assignment of the cutoff masses for the BBM-vertices is dependent on the SU(3)-classification of the exchanged mesons for OBE, and a similar scheme for MPE. The ESC-models ESC04 and ESC08 describe the nucleon-nucleon (NN), hyperon-nucleon (YN), and hyperon-hyperon (YY) interactions in a unified way using broken SU(3)-symmetry. Novel ingredients in the OBE-sector in the ESC-models are the inclusion of (i) the axial-vector meson potentials, (ii) a zero in the scalar- and axial-vector meson form factors. These innovations made it possible for the first time to keep the meson coupling parameters of the model qualitatively in accordance with the predictions of the (3P_0) quark-antiquark creation (QPC) model. This is also the case for the F/(F+D)-ratios. Furthermore, the introduction of the zero helped to avoid the occurrence of unwanted bound states in Lambda N. Broken SU(3)-symmetry serves to connect the NN and the YN channels, which leaves after fitting NN only a few free parameters for the determination of the YN-interactions. In particular, the meson-baryon coupling constants are calculated via SU(3) using the coupling constants of the NN oplus YN-analysis as input. In ESC04 medium strong flavor-symmetry-breaking (FSB) of the coupling constants was investigated, using the (3}P_{0) -model with a Gell-Mann-Okubo hypercharge breaking for the BBM-coupling. In ESC08 the couplings are kept SU(3)-symmetric. The charge-symmetry-breaking (CSB) in the Lambda p and Lambda n channels, which is an SU(2) isospin breaking, is included in the OBE-, TME-, and MPE-potentials. In ESC04 and ESC08 simultaneous fits to the NN- and the YN- scattering data have been achieved, using different options for the ESC-model. In particularly in ESC08 with single-sets of parameters excellent fits were obtained for the NN- and YN-data. For example, in the case of ESC08a'' we have: (i) For the selected 4233 NN-data with energies 0 ≤ T_{lab} ≤ 350 MeV, excellent results were obtained having chi(2/N_{data}) = 1.094. (ii) For the usual set of 35 YN-data and 3 Sigma(+p) cross-sections from a recent KEK-experiment E289 [H. Kanda et al., AIP Conf. Proc. 842 (2006), 501; H. Kanda, Measurement of the cross sections of Sigma(=p) elastic scattering, Ph. D. thesis, Department of Physics, Faculty of Science, Kyoto University, March 2007] the fit has chi(2}/YN_{data) ≈ 0.83. (iii) For YY there is a weak LambdaLambda-interaction, which successfully matches with t he Nagara-event [H. Takahashi et al., Phys. Rev. Lett. 87 (2001), 212502]. (iv) The nuclear Sigma and Xi well-dephts satisfy U_Sigma > 0 and U_Xi < 0. The predictions for the S = -2 (LambdaLambda, Xi N, LambdaSigma, SigmaSigma)-channels are the occurrences of an S = -2 bound states in the Xi N((3S_1-^3D_1,) I = 0,1)-channels.

Implementation of an Adaptive Controller System from Concept to Flight Test

NASA Technical Reports Server (NTRS)

Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

2009-01-01

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a "floating limiter" (FL) concept that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly "floating" and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually "hit" the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

Neural Net Safety Monitor Design

NASA Technical Reports Server (NTRS)

Larson, Richard R.

2007-01-01

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

Resistance change effect in SrTiO3/Si (001) isotype heterojunction

NASA Astrophysics Data System (ADS)

Huang, Xiushi; Gao, Zhaomeng; Li, Pei; Wang, Longfei; Liu, Xiansheng; Zhang, Weifeng; Guo, Haizhong

2018-02-01

Resistance switching has been observed in double and multi-layer structures of ferroelectric films. The higher switching ratio opens up a vast path for emerging ferroelectric semiconductor devices. An n-n+ isotype heterojunction has been fabricated by depositing an oxide SrTiO3 layer on a conventional n-type Si (001) substrate (SrTiO3/Si) by pulsed laser disposition. Rectification and resistive switching behaviors in the n-n+ SrTiO3/Si heterojunction were observed by a conductive atomic force microscopy, and the n-n+ SrTiO3/Si heterojunction exhibits excellent endurance and retention characteristics. The possible mechanism was proposed based on the band structure of the n-n+ SrTiO3/Si heterojunction, and the observed electrical behaviors could be attributed to the modulation effect of the electric field reversal on the width of accumulation and the depletion region, as well as the height of potential of the n-n+ junction formed at the STO/Si interface. Moreover, oxygen vacancies are also indicated to play a crucial role in causing insulator to semiconductor transition. These results open the way to potential application in future microelectronic devices based on perovskite oxide layers on conventional semiconductors.

The effects of ryanodine receptor (RYR1) mutation on natural killer cell cytotoxicity, plasma cytokines and stress hormones during acute intermittent exercise in pigs.

PubMed

Ciepielewski, Z M; Stojek, W; Borman, A; Myślińska, D; Pałczyńska, P; Kamyczek, M

2016-04-01

Stress susceptibility has been mapped to a single recessive gene, the ryanodine receptor 1 (RYR1) gene or halothane (Hal) gene. Homozygous (Hal(nn)), mutated pigs are sensitive to halothane and susceptible to Porcine Stress Syndrome (PSS). Previous studies have shown that stress-susceptible RYR1 gene mutated homozygotes in response to restraint stress showed an increase in natural killer cell cytotoxicity (NKCC) accompanied by more pronounced stress-related hormone and anti-inflammatory cytokine changes. In order to determine the relationship of a RYR1 gene mutation with NKCC, plasma cytokines and stress-related hormones following a different stress model - exercise - 36 male pigs (representing different genotypes according to RYR1 gene mutation: NN, homozygous dominant; Nn, heterozygous; nn, homozygous recessive) were submitted to an intermittent treadmill walking. During the entire experiment the greatest level of NKCC and the greatest concentrations of interleukin (IL-) 6, IL-10, IL-12, interferon (IFN-)γ and tumor necrosis factor-α and stress-related hormones (adrenaline, prolactin, beta-endorphin) were observed in nn pigs, and the greatest concentration of IL-1 and growth hormone in NN pigs. Immunostimulatory effects of intermittent exercise on NKCC in nn pigs were concomitant with increases in IL-2, IL-12 and IFN-γ, the potent NKCC activators. Our findings suggest that stress-susceptible pigs RYR1 gene mutated pigs develop a greater level of NKCC and cytokine production in response to exercise stress. These results suggest that the heterogeneity of immunological and neuroendocrine response to exercise stress in pigs could be influenced by RYR1 gene mutation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

NASA Technical Reports Server (NTRS)

Aires, F.; Prigent, C.; Rossow, W. B.

2004-01-01

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Measuring KS0 K± interactions using Pb-Pb collisions at √{sNN} = 2.76 TeV

NASA Astrophysics Data System (ADS)

Acharya, S.; Adamová, D.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, N.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Alba, J. L. B.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altenkamper, L.; Altsybeev, I.; Alves Garcia Prado, C.; An, M.; Andrei, C.; Andreou, D.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C.; Antičić, T.; Antinori, F.; Antonioli, P.; Anwar, R.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Ball, M.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barioglio, L.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biswas, R.; Biswas, S.; Blair, J. T.; Blau, D.; Blume, C.; Boca, G.; Bock, F.; Bogdanov, A.; Boldizsár, L.; Bombara, M.; Bonomi, G.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Capon, A. A.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cerello, P.; Chandra, S.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Chowdhury, T.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Concas, M.; Conesa Balbastre, G.; Conesa Del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Costanza, S.; Crkovská, J.; Crochet, P.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; de, S.; de Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; de Falco, A.; de Gruttola, D.; De Marco, N.; de Pasquale, S.; de Souza, R. D.; Degenhardt, H. F.; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; di Bari, D.; di Mauro, A.; di Nezza, P.; di Ruzza, B.; Diakonov, I.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Doremalen, L. V. V.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Fabbietti, L.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Garg, P.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Gay Ducati, M. B.; Germain, M.; Ghosh, J.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Greiner, L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grion, N.; Gronefeld, J. M.; Grosa, F.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hassan, H.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hills, C.; Hippolyte, B.; Hladky, J.; Hohlweger, B.; Horak, D.; Hornung, S.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Iga Buitron, S. A.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Irfan, M.; Isakov, V.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jaelani, S.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jercic, M.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Ketzer, B.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kielbowicz, M. M.; Kileng, B.; Kim, D.; Kim, D. W.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Konyushikhin, M.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lai, Y. S.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lavicka, R.; Lazaridis, L.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lim, B.; Lindal, S.; Lindenstruth, V.; Lindsay, S. W.; Lippmann, C.; Lisa, M. A.; Litichevskyi, V.; Ljunggren, H. M.; Llope, W. J.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Loncar, P.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martinez, J. A. L.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Masson, E.; Mastroserio, A.; Mathis, A. M.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mihaylov, D.; Mihaylov, D. L.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Mohisin Khan, M.; Montes, E.; Moreira de Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Myers, C. J.; Myrcha, J. W.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Narayan, A.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao de Oliveira, R. A.; Nellen, L.; Nesbo, S. V.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Nobuhiro, A.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Ohlson, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Pachmayer, Y.; Pacik, V.; Pagano, D.; Pagano, P.; Paić, G.; Palni, P.; Pan, J.; Pandey, A. K.; Panebianco, S.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Park, W. J.; Parmar, S.; Passfeld, A.; Pathak, S. P.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira, L. G.; Pereira da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Pezzi, R. P.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pliquett, F.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Porter, J.; Pozdniakov, V.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Rana, D. B.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Rokita, P. S.; Ronchetti, F.; Rosnet, P.; Rossi, A.; Rotondi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rueda, O. V.; Rui, R.; Russo, R.; Rustamov, A.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Saha, S. K.; Sahlmuller, B.; Sahoo, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandoval, A.; Sarkar, D.; Sarkar, N.; Sarma, P.; Sas, M. H. P.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Scheid, H. S.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M. O.; Schmidt, M.; Schuchmann, S.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sett, P.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shahoyan, R.; Shaikh, W.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thakur, D.; Thakur, S.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Tropp, L.; Trubnikov, V.; Trzaska, W. H.; Trzeciak, B. A.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Umaka, E. N.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; van der Maarel, J.; van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wenzel, S. C.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Willsher, E.; Windelband, B.; Witt, W. E.; Yalcin, S.; Yamakawa, K.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.; Zou, S.; Alice Collaboration

2017-11-01

We present the first ever measurements of femtoscopic correlations between the KS0 and K± particles. The analysis was performed on the data from Pb-Pb collisions at √{sNN} = 2.76 TeV measured by the ALICE experiment. The observed femtoscopic correlations are consistent with final-state interactions proceeding via the a0 (980) resonance. The extracted kaon source radius and correlation strength parameters for KS0 K- are found to be equal within the experimental uncertainties to those for KS0 K+. Comparing the results of the present study with those from published identical-kaon femtoscopic studies by ALICE, mass and coupling parameters for the a0 resonance are tested. Our results are also compatible with the interpretation of the a0 having a tetraquark structure instead of that of a diquark.

Measuring K S 0 K ± interactions using Pb–Pb collisions at s NN = 2.76 TeV

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

Acharya, S.; Adamová, D.; Adolfsson, J.

We present the first ever measurements of femtoscopic correlations between the K S 0 and K ± particles. The analysis was performed on the data from Pb–Pb collisions at √ sNN =2.76 TeV measured by the ALICE experiment. The observed femtoscopic correlations are consistent with final-state interactions proceeding via the a 0(980) resonance. The extracted kaon source radius and correlation strength parameters for K S 0K - are found to be equal within the experimental uncertainties to those for K S 0K +. Comparing the results of the present study with those from published identical-kaon femtoscopic studies by ALICE, massmore » and coupling parameters for the a 0 resonance are tested. Our results are also compatible with the interpretation of the a 0 having a tetraquark structure instead of that of a diquark.« less

Measuring K S 0 K ± interactions using Pb–Pb collisions at s NN = 2.76 TeV

DOE PAGES

Acharya, S.; Adamová, D.; Adolfsson, J.; ...

2017-09-08

We present the first ever measurements of femtoscopic correlations between the K S 0 and K ± particles. The analysis was performed on the data from Pb–Pb collisions at √ sNN =2.76 TeV measured by the ALICE experiment. The observed femtoscopic correlations are consistent with final-state interactions proceeding via the a 0(980) resonance. The extracted kaon source radius and correlation strength parameters for K S 0K - are found to be equal within the experimental uncertainties to those for K S 0K +. Comparing the results of the present study with those from published identical-kaon femtoscopic studies by ALICE, massmore » and coupling parameters for the a 0 resonance are tested. Our results are also compatible with the interpretation of the a 0 having a tetraquark structure instead of that of a diquark.« less

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

NASA Technical Reports Server (NTRS)

Padgett, Mary L. (Editor)

1993-01-01

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Erosion and deposition mode in a developing foreland basin: Temporal and spatial distribution of provenance in southwestern Taiwan

NASA Astrophysics Data System (ADS)

Yang, K. M.; Kun-an, H.; Chien, C. W.; Leh-chyun, W.; Chi-Cheng, Y.

2017-12-01

The foreland basin in southwestern Taiwan offers an idealistic example for the study of tectonostratigraphy in basin development. The subsidence analysis indicates that the recent basin development went through at least two rapid subsidence events, along with back-and-forth migration of the forebulge. This study aims to explore the interaction between the uplifting forebulge and coevally subsiding foredeep primarily based on petrofacies analysis, the results of which were then interpreted with the well-established tectonostratigraphic and biostratigraphic frameworks to infer the erosion and deposition mode during the basin development. The craton had been the sediment source to the west of the study area in the pre-orogenic period. In the initial stage of foreland basin development, the forebulge slowly elevated and started to obstruct sediment supplies from the craton. Before the period of NN19, the forebulge not only became the barrier of the most cratonic sediment supplies but also shed a major amount of detritus into the adjacent area. In addition, regional topographic relief, which was formed by syn-orogenic normal faulting during the NN11-15, locally changed the composition and transportation modes of the sediments; the exposed basement of the footwall also became the source of the sediments shed into the adjacent depo-centers. After the NN19, whole area was influenced predominantly by the orogenic belt from the east. Large amounts of slate fragments began to appear in the middle NN19 and relative percentage of the metamorphic lithics was increased upward and northward. As the orogen moved westward along with the foreland basin development, the studied area changed from the distal to proximal parts of the foredeep and sediment sources were controlled mainly by river systems derived from the orogen. The metamorphic lithics decreased southward and concentrated in the central part of the study area, suggesting that the slate fragments which were transported parallel with the orientation of submarine canyons since NN13 to the south of the study area. We propose that 1) from NN13 to NN18, the episodic subsidence in the foreland basin implies episodic movement of the orogenic belt, and 2) since the period of NN19, the orogenic belt and foreland basin has been developing in a continuous and steady state.

Characterization and neutralization of Nemopilema nomurai (Scyphozoa: Rhizostomeae) jellyfish venom using polyclonal antibody.

PubMed

Kang, Changkeun; Han, Dae-Yong; Park, Kwang-Il; Pyo, Min-Jung; Heo, Yunwi; Lee, Hyunkyoung; Kim, Gon Sup; Kim, Euikyung

2014-08-01

Jellyfish stings have often caused serious health concerns for sea bathers especially in tropical waters. In the coastal areas of Korea, China and Japan, the blooming and stinging accidents of poisonous jellyfish species have recently increased, including Nemopilema nomurai. We have generated a polyclonal antibody against N. nomurai jellyfish venom (NnV) by the immunization of white rabbits with NnV antigen. In the present study, the antibody has been characterized for its neutralizing effect against NnV. At first, the presence of NnV polyclonal antibody has been confirmed from the immunized rabbit serum by Enzyme linked immunosorbent assay (ELISA). Then, the neutralizing activities of the polyclonal antibody have been investigated using cell-based toxicity test, hemolysis assay, and mice lethality test. When the polyclonal antibody was preincubated with NnV, it shows a high effectiveness in neutralizing the NnV toxicities in a concentration-dependent manner. Moreover, we explored proteomic analyses using 2-D SDS-PAGE and MALDI-TOF mass spectrometry to illustrate the molecular identities of the jellyfish venom. From this, 18 different protein families have been identified as jellyfish venom-derived proteins; the main findings of which are matrix metalloproteinase-14, astacin-like metalloprotease toxin 3 precursor. It is expected that the present results would have contributed to our understandings of the envenomation by N. nomurai, their treatment and some valuable knowledge on the pathological processes of the jellyfish stinging. Copyright © 2014 Elsevier Ltd. All rights reserved.

Strange baryon resonance production in sqrt s NN=200 GeV p+p and Au+Au collisions.

PubMed

Abelev, B I; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Anderson, M; Arkhipkin, D; Averichev, G S; Bai, Y; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellingeri-Laurikainen, A; Bellwied, R; Benedosso, F; Bhardwaj, S; Bhasin, A; Bhati, A K; Bichsel, H; Bielcik, J; Bielcikova, J; Bland, L C; Blyth, S-L; Bonner, B E; Botje, M; Bouchet, J; Brandin, A V; Bravar, A; Burton, T P; Bystersky, M; Cadman, R V; Cai, X Z; Caines, H; Calderón de la Barca Sánchez, M; Castillo, J; Catu, O; Cebra, D; Chajecki, Z; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, J H; Cheng, J; Cherney, M; Chikanian, A; Christie, W; Coffin, J P; Cormier, T M; Cosentino, M R; Cramer, J G; Crawford, H J; Das, D; Das, S; Dash, S; Daugherity, M; de Moura, M M; Dedovich, T G; DePhillips, M; Derevschikov, A A; Didenko, L; Dietel, T; Djawotho, P; Dogra, S M; Dong, W J; Dong, X; Draper, J E; Du, F; Dunin, V B; Dunlop, J C; Dutta Mazumdar, M R; Eckardt, V; Edwards, W R; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Fatemi, R; Fedorisin, J; Filimonov, K; Filip, P; Finch, E; Fine, V; Fisyak, Y; Fu, J; Gagliardi, C A; Gaillard, L; Ganti, M S; Gaudichet, L; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Gorbunov, Y G; Gos, H; Grebenyuk, O; Grosnick, D; Guertin, S M; Guimaraes, K S F F; Gupta, N; Gutierrez, T D; Haag, B; Hallman, T J; Hamed, A; Harris, J W; He, W; Heinz, M; Henry, T W; Hepplemann, S; Hippolyte, B; Hirsch, A; Hjort, E; Hoffman, A M; Hoffmann, G W; Horner, M J; Huang, H Z; Huang, S L; Hughes, E W; Humanic, T J; Igo, G; Jacobs, P; Jacobs, W W; Jakl, P; Jia, F; Jiang, H; Jones, P G; Judd, E G; Kabana, S; Kang, K; Kapitan, J; Kaplan, M; Keane, D; Kechechyan, A; Khodyrev, V Yu; Kim, B C; Kiryluk, J; Kisiel, A; Kislov, E M; Klein, S R; Kocoloski, A; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kouchpil, V; Kowalik, K L; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; LaPointe, S; Laue, F; Lauret, J; Lebedev, A; Lednicky, R; Lee, C-H; Lehocka, S; LeVine, M J; Li, C; Li, Q; Li, Y; Lin, G; Lin, X; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, H; Liu, J; Liu, L; Liu, Z; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Love, W A; Lu, Y; Ludlam, T; Lynn, D; Ma, G L; Ma, J G; Ma, Y G; Magestro, D; Mahapatra, D P; Majka, R; Mangotra, L K; Manweiler, R; Margetis, S; Markert, C; Martin, L; Matis, H S; Matulenko, Yu A; McClain, C J; McShane, T S; Melnick, Yu; Meschanin, A; Millane, J; Miller, M L; Minaev, N G; Mioduszewski, S; Mironov, C; Mischke, A; Mishra, D K; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Morozov, D A; Munhoz, M G; Nandi, B K; Nattrass, C; Nayak, T K; Nelson, J M; Netrakanti, P K; Nogach, L V; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Pachr, M; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Peitzmann, T; Perevoztchikov, V; Perkins, C; Peryt, W; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Poljak, N; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rakness, G; Raniwala, R; Raniwala, S; Ray, R L; Razin, S V; Reinnarth, J; Relyea, D; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevskiy, O V; Romero, J L; Rose, A; Roy, C; Ruan, L; Russcher, M J; Sahoo, R; Sakuma, T; Salur, S; Sandweiss, J; Sarsour, M; Sazhin, P S; Schambach, J; Scharenberg, R P; Schmitz, N; Schweda, K; Seger, J; Selyuzhenkov, I; Seyboth, P; Shabetai, A; Shahaliev, E; Shao, M; Sharma, M; Shen, W Q; Shimanskiy, S S; Sichtermann, E; Simon, F; Singaraju, R N; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Speltz, J; Spinka, H M; Srivastava, B; Stadnik, A; Stanislaus, T D S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Suaide, A A P; Sugarbaker, E; Sumbera, M; Sun, Z; Surrow, B; Swanger, M; Symons, T J M; Szanto de Toledo, A; Tai, A; Takahashi, J; Tang, A H; Tarnowsky, T; Thein, D; Thomas, J H; Timmins, A R; Timoshenko, S; Tokarev, M; Trainor, T A; Trentalange, S; Tribble, R E; Tsai, O D; Ulery, J; Ullrich, T; Underwood, D G; Buren, G Van; van der Kolk, N; van Leeuwen, M; Molen, A M Vander; Varma, R; Vasilevski, I M; Vasiliev, A N; Vernet, R; Vigdor, S E; Viyogi, Y P; Vokal, S; Voloshin, S A; Waggoner, W T; Wang, F; Wang, G; Wang, J S; Wang, X L; Wang, Y; Watson, J W; Webb, J C; Westfall, G D; Wetzler, A; Whitten, C; Wieman, H; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Q H; Xu, Z; Yepes, P; Yoo, I-K; Yurevich, V I; Zhan, W; Zhang, H; Zhang, W M; Zhang, Y; Zhang, Z P; Zhao, Y; Zhong, C; Zoulkarneev, R; Zoulkarneeva, Y; Zubarev, A N; Zuo, J X

2006-09-29

We report the measurements of Sigma(1385) and Lambda(1520) production in p+p and Au+Au collisions at sqrt[s{NN}]=200 GeV from the STAR Collaboration. The yields and the p(T) spectra are presented and discussed in terms of chemical and thermal freeze-out conditions and compared to model predictions. Thermal and microscopic models do not adequately describe the yields of all the resonances produced in central Au+Au collisions. Our results indicate that there may be a time span between chemical and thermal freeze-out during which elastic hadronic interactions occur.

A case of persistent retrograde amnesia following a dissociative fugue: neuropsychological and neurofunctional underpinnings of loss of autobiographical memory and self-awareness.

PubMed

Hennig-Fast, Kristina; Meister, Franziska; Frodl, Thomas; Beraldi, Anna; Padberg, Frank; Engel, Rolf R; Reiser, Maximilian; Möller, Hans-Jürgen; Meindl, Thomas

2008-10-01

Autobiographical memory relies on complex interactions between episodic memory contents, associated emotions and a sense of self-continuity over the course of one's life. This paper reports a study based upon the case of the patient NN who suffered from a complete loss of autobiographical memory and awareness of identity subsequent to a dissociative fugue. Neuropsychological, behavioral, and functional neuroimaging tests converged on the conclusion that NN suffered from a selective retrograde amnesia following an episode of dissociative fugue, during which he had lost explicit knowledge and vivid memory of his personal past. NN's loss of self-related memories was mirrored in neurobiological changes after the fugue whereas his semantic memory remained intact. Although NN still claimed to suffer from a stable loss of autobiographical, self-relevant memories 1 year after the fugue state, a proportionate improvement in underlying fronto-temporal neuronal networks was evident at this point in time. In spite of this improvement in neuronal activation, his anterograde visual memory had been decreased. It is posited that our data provide evidence for the important role of visual processing in autobiographical memory as well as for the efficiency of protective control mechanisms that constitute functional retrograde amnesia.

Elliptic flow of electrons from heavy-flavor hadron decays in Au + Au collisions at s N N = 200 , 62.4, and 39 GeV

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

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.

Here, we present measurements of elliptic flow (v 2) of electrons from the decays of heavy-flavor hadrons (e HF) by the STAR experiment. For Au+Au collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=200 GeV we report v 2, for transverse momentum (p T) between 0.2 and 7 GeV/c, using three methods: the event plane method (v 2{EP}), two-particle correlations (v 2{2}), and four-particle correlations (v 2{4}). For Au+Au collisions at $$\\sqrt{s}$$$_ {NN}$$=62.4 and 39 GeV we report v 2{2} for p T <2GeV/c. v 2{2} and v 2{4} are nonzero at low and intermediate p T at 200 GeV, and v 2{2} is consistent with zero at low p T at other energies. The v 2{2} at the two lower beam energies is systematically lower than at $$\\sqrt{s}$$$_ {NN}$$=200 GeV for p T <1GeV/c. This difference may suggest that charm quarks interact less strongly with the surrounding nuclear matter at those two lower energies compared to $$\\sqrt{s}$$$_ {NN}$$=200 GeV.« less

Elliptic flow of electrons from heavy-flavor hadron decays in Au + Au collisions at s N N = 200 , 62.4, and 39 GeV

DOE PAGES

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; ...

2017-03-13

Here, we present measurements of elliptic flow (v 2) of electrons from the decays of heavy-flavor hadrons (e HF) by the STAR experiment. For Au+Au collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=200 GeV we report v 2, for transverse momentum (p T) between 0.2 and 7 GeV/c, using three methods: the event plane method (v 2{EP}), two-particle correlations (v 2{2}), and four-particle correlations (v 2{4}). For Au+Au collisions at $$\\sqrt{s}$$$_ {NN}$$=62.4 and 39 GeV we report v 2{2} for p T <2GeV/c. v 2{2} and v 2{4} are nonzero at low and intermediate p T at 200 GeV, and v 2{2} is consistent with zero at low p T at other energies. The v 2{2} at the two lower beam energies is systematically lower than at $$\\sqrt{s}$$$_ {NN}$$=200 GeV for p T <1GeV/c. This difference may suggest that charm quarks interact less strongly with the surrounding nuclear matter at those two lower energies compared to $$\\sqrt{s}$$$_ {NN}$$=200 GeV.« less

Effect of repulsive and attractive three-body forces on nucleus-nucleus elastic scattering

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

Furumoto, T.; Sakuragi, Y.; Yamamoto, Y.

2009-10-15

The effect of the three-body force (TBF) is studied in nucleus-nucleus elastic scattering on the basis of Brueckner theory for nucleon-nucleon (NN) effective interaction (complex G matrix) in the nuclear matter. A new G matrix called CEG07 proposed recently by the present authors includes the TBF effect and reproduces a realistic saturation curve in the nuclear matter, and it is shown to well reproduce proton-nucleus elastic scattering. The microscopic optical potential for the nucleus-nucleus system is obtained by folding the G matrix with nucleon density distributions in colliding nuclei. We first analyze in detail the {sup 16}O+{sup 16}O elastic scatteringmore » at E/A=70 MeV. The observed cross sections are nicely reproduced up to the most backward scattering angles only when the TBF effect is included. The use of the frozen-density approximation (FDA) is essentially important to properly estimate the effect of the TBF in nucleus-nucleus scattering. Other prescriptions for defining the local density have also been tested, but only the FDA prescription gives a proper description of the experimental cross sections as well as the effect of the TBF. The effects of the three-body attraction and the {omega}-rearrangement term are also analyzed. The CEG07 interaction is compared with CDM3Y6, which is a reliable and successful effective density-dependent NN interaction used in the double-folding model. The CEG07 G matrix is also tested in the elastic scattering of {sup 16}O by the {sup 12}C, {sup 28}Si, and {sup 40}Ca targets at E/A=93.9 MeV, and in the elastic scattering of {sup 12}C by the {sup 12}C target at E/A=135 MeV with great success. The decisive effect of the TBF is clearly seen also in those systems. Finally, we have tested CEG07a, CEG07b, and CEG07c for the {sup 16}O+{sup 16}O system at various energies.« less

Technical note: an R package for fitting sparse neural networks with application in animal breeding.

PubMed

Wang, Yangfan; Mi, Xue; Rosa, Guilherme J M; Chen, Zhihui; Lin, Ping; Wang, Shi; Bao, Zhenmin

2018-05-04

Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.

The distance function effect on k-nearest neighbor classification for medical datasets.

PubMed

Hu, Li-Yu; Huang, Min-Wei; Ke, Shih-Wen; Tsai, Chih-Fong

2016-01-01

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually. The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets. In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Altered processing of the neurotensin/neuromedin N precursor in PC2 knock down mice: a biochemical and immunohistochemical study.

PubMed

Villeneuve, Pierre; Feliciangeli, Sylvain; Croissandeau, Gilles; Seidah, Nabil G; Mbikay, Majambu; Kitabgi, Patrick; Beaudet, Alain

2002-08-01

Neurotensin (NT) and neuromedin N (NN) are generated by endoproteolytic cleavage of a common precursor molecule, pro-NT/NN. To gain insight into the role of prohormone convertases PC1, PC2, and PC7 in this process, we investigated the maturation of pro-NT/NN in the brain of PC7 (PC7-/-), PC2 (PC2-/-), and/or PC1 (PC1+/- and PC2-/-; PC1+/-) knock down mice. Inactivation of the PC7 gene was without effect, suggesting that this convertase is not involved in the processing of pro-NT/NN. By contrast, there was a 15% decrease in NT and a 50% decrease in NN levels, as measured by radioimmunoassay, in whole brain extracts from PC2 null as compared with wild type mice. Using immunohistochemistry, we found that this decrease in pro-NT/NN maturation products was uneven and that it was most pronounced in the medial preoptic area, lateral hypothalamus, and paraventricular hypothalamic nuclei. These results suggest that PC2 plays a critical role in the processing of pro-NT/NN in mouse brain and that its deficiency may be compensated to a regionally variable extent by other convertases. Previous data have suggested that PC1 might be subserving this role. However, there was no change in the maturation of pro-NT/NN in the brain of mice in which the PC1 gene had been partially inactivated, implying that complete PC1 knock down may be required for loss of function.

Triggering Mechanism for Neutron Induced Single-Event Burnout in Power Devices

NASA Astrophysics Data System (ADS)

Shoji, Tomoyuki; Nishida, Shuichi; Hamada, Kimimori

2013-04-01

Cosmic ray neutrons can trigger catastrophic failures in power devices. It has been reported that parasitic transistor action causes single-event burnout (SEB) in power metal-oxide-semiconductor field-effect transistors (MOSFETs) and insulated gate bipolar transistors (IGBTs). However, power diodes do not have an inherent parasitic transistor. In this paper, we describe the mechanism triggering SEB in power diodes for the first time using transient device simulation. Initially, generated electron-hole pairs created by incident recoil ions generate transient current, which increases the electron density in the vicinity of the n-/n+ boundary. The space charge effect of the carriers leads to an increase in the strength of the electric field at the n-/n+ boundary. Finally, the onset of impact ionization at the n-/n+ boundary can trigger SEB. Furthermore, this failure is closely related to diode secondary breakdown. It was clarified that the impact ionization at the n-/n+ boundary is a key point of the mechanism triggering SEB in power devices.

S-wave Approach for \\varvec{nnp} and \\varvec{ppn} Systems with Phenomenological Correction for Singlet \\varvec{NN} Potentials

NASA Astrophysics Data System (ADS)

Vlahovic, B.; Suslov, V. M.; Filikhin, I.

2017-03-01

Three-nucleon systems are considered assuming the neutrons and protons to be distinguishable particles. The configuration space Faddeev equations within the s-wave approach are applied for studying bound state and scattering problems. The phenomenological Malfliet-Tjon MT I-III and Afnan-Tang ATS3 NN potentials are used with scaling factors chosen to reproduce the singlet nn, pp and np experimental scattering lengths. Numerical evaluation for the charge symmetry breaking energy is found to be about 50 keV for ^3H and ^3He nuclei. To determine any effects related to the nn ( pp) and np potential differences, the nd and pd breakup scattering calculations were performed at E_{lab}=4.0 and 14.1 MeV. We found the effects due to potential differences are small but noticeable. We discuss the dependence of calculated inelasticities and phase-shifts with respect to the chosen value for cutoff radius.

Constraining in-medium nucleon-nucleon interactions via nucleus-nucleus reactions

NASA Astrophysics Data System (ADS)

Sammarruca, Francesca; White, Larz

2010-11-01

The nuclear equation of state is a broadly useful tool. Besides being the main input of stellar structure calculations, it allows a direct connection to the physics of nuclei. For instance, an energy functional (such as a mass formula), together with the energy/particle in nuclear matter, can be used to predict nuclear energies and radii [1]. The single-particle properties are also a key point to link infinite nuclear matter and actual nuclei. The parameters of the single-particle potential, in particular the effective mass, enter the calculations of, for instance, in-medium effective cross sections. From the well-known Glauber reaction theory, the total nucleus-nucleus reaction cross section is expressed in terms of the nuclear transparency, which, in turn, depends on the overlap of the nuclear density distributions and the elementary nucleon-nucleon (NN) cross sections. We explore the sensitivity of the reaction calculation to medium modifications of the NN cross sections to estimate the likelihood of constraining the latter through nuclear reactions. Ultimately, we wish to incorporate isospin asymmetry in the reaction model, having in mind connections with rare isotopes. [1] F. Sammarruca, arXiv:1002.00146 [nucl-th]; International Journal of Modern Physics, in press.

Performance of local correlation methods for halogen bonding: The case of Br{sub 2}–(H{sub 2}O){sub n},n = 4,5 clusters and Br{sub 2}@5{sup 12}6{sup 2} clathrate cage

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

Batista-Romero, Fidel A.; Bernal-Uruchurtu, Margarita I.; Hernández-Lamoneda, Ramón, E-mail: ramon@uaem.mx

The performance of local correlation methods is examined for the interactions present in clusters of bromine with water where the combined effect of hydrogen bonding (HB), halogen bonding (XB), and hydrogen-halogen (HX) interactions lead to many interesting properties. Local methods reproduce all the subtleties involved such as many-body effects and dispersion contributions provided that specific methodological steps are followed. Additionally, they predict optimized geometries that are nearly free of basis set superposition error that lead to improved estimates of spectroscopic properties. Taking advantage of the local correlation energy partitioning scheme, we compare the different interaction environments present in small clustersmore » and those inside the 5{sup 12}6{sup 2} clathrate cage. This analysis allows a clear identification of the reasons supporting the use of local methods for large systems where non-covalent interactions play a key role.« less

An efficient synthetic method for organometallic radicals: structures and properties of gold(i)-(nitronyl nitroxide)-2-ide complexes.

PubMed

Suzuki, Shuichi; Kira, Sayaka; Kozaki, Masatoshi; Yamamura, Masaki; Hasegawa, Toru; Nabeshima, Tatsuya; Okada, Keiji

2017-02-21

One-pot synthesis of (nitronyl nitroxide)-gold(i)-phosphine (NN-Au-P) complexes has been developed using chloro(tetrahydrothiophene)gold(i), phosphine ligands, nitronyl nitroxide radicals, and sodium hydroxide. The NN-Au-P complexes can be easily handled because they were quite stable under aerated conditions in both solution and crystalline states. They showed weak absorption bands with vibrational structures in the 450-650 nm region. The oxidation potentials assigned to the NN moieties of NN-Au-P complexes with aromatic phosphines were observed around -0.1 V vs. Fc/Fc + (-0.11 V for NN-Au-1, -0.08 V for NN-Au-2, -0.13 V for NN-Au-5, and -0.07 V for NN-Au-6), somewhat lower than that of NN-Au-P complexes with aliphatic phosphines (-0.25 V for NN-Au-3 and -0.17 V for NN-Au-4).

Systematics of α-decay fine structure in odd-mass nuclei based on a finite-range nucleon-nucleon interaction

NASA Astrophysics Data System (ADS)

Adel, A.; Alharbi, T.

2018-07-01

A systematic study on α-decay fine structure is presented for odd-mass nuclei in the range 83 ≤ Z ≤ 92. The α-decay partial half-lives and branching ratios to the ground and excited states of daughter nuclei are calculated in the framework of the Wentzel-Kramers-Brillouin (WKB) approximation with the implementation of the Bohr-Sommerfeld quantization condition. The microscopic α-daughter potential is obtained using the double-folding model with a realistic M3Y-Paris nucleon-nucleon (NN) interaction. The exchange potential, which accounts for the knock-on exchange of nucleons between the interacting nuclei, is calculated using the finite-range exchange NN interaction which is essentially a much better approximation as compared to the zero-range pseudo-potential adopted in the usual double-folding calculations. Our calculations of α-decay fine structure have been improved by considering the preformation factor extracted from the recently proposed cluster formation model on basis of the binding energy difference. The computed partial half-lives and branching ratios are compared with the recent experimental data and they are in good agreement.

Determination of the Nucleon-Nucleon Interaction in the ImQMD Model by Nuclear Reactions at Fermi Energy

NASA Astrophysics Data System (ADS)

Li, Cheng; Tian, Jun-Long; Wang, Ning

2013-11-01

The nucleon-nucleon interaction is investigated by using the ImQMD model with the three sets of parameters IQ1, IQ2 and IQ3 in which the corresponding incompressibility coefficients of nuclear matter are different. Fusion excitation function and the charge distribution of fragments are calculated for reaction systems 40Ca+40Ca at different incident energies. It is found that obvious differences in the charge distribution were observed at the energy region 10-25A MeV by adopting the three sets of parameters, while the results were close to each other at energy region of 30-45A MeV for the reaction system. It indicates that the Fermi energy region is a sensitive energy region to explore the N-N interaction. The fragment multiplicity spectrum for 238U+197Au at 15A MeV are reproduced by the ImQMD model with the set of parameter IQ3. It is concluded that charge distribution of the fragments and the fragment multiplicity spectrum are good observables for studying N-N interaction, and IQ3 is a suitable set of parameters for the ImQMD model.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

NASA Astrophysics Data System (ADS)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Attenuation of neurobehavioral and neurochemical abnormalities in animal model of cognitive deficits of Alzheimer's disease by fermented soybean nanonutraceutical.

PubMed

Bhatt, Prakash Chandra; Pathak, Shruti; Kumar, Vikas; Panda, Bibhu Prasad

2018-02-01

The present study was performed to evaluate the efficacy of nanonutraceuticals (NN) for attenuation of neurobehavioral and neurochemical abnormalities in Alzheimer's disease. Solid-state fermentation of soybean with Bacillus subtilis was performed to produce different metabolites (nattokinase, daidzin, genistin and glycitin and menaquinone-7). Intoxication of rats with colchicine caused impairment in learning and memory which was demonstrated in neurobehavioral paradigms (Morris water maze and passive avoidance) linked with decreased activity of acetylcholinesterase (AChE). NN treatment led to a significant increase in TLT in the retention trials as compared to acquisition trial TLT suggesting an improved learning and memory in rats. Further, treatment of NN caused an increase in the activity of AChE (42%), accompanied with a reduced activity of glutathione (42%), superoxide dismutase (43%) and catalase (41%). It also decreased the level of lipid peroxidation (28%) and protein carbonyl contents (30%) in hippocampus as compared to those treated with colchicine alone, suggesting a possible neuroprotective efficacy of NN. Interestingly, in silico studies also demonstrated an effective amyloid-β and BACE-1 inhibition activity. These findings clearly indicated that NN reversed colchicine-induced behavioral and neurochemical alterations through potent antioxidant activity and could possibly impart beneficial effects in cognitive defects associated with Alzheimer's disease.

Mean-field study of hot β -stable protoneutron star matter: Impact of the symmetry energy and nucleon effective mass

NASA Astrophysics Data System (ADS)

Tan, Ngo Hai; Loan, Doan Thi; Khoa, Dao T.; Margueron, Jerome

2016-03-01

A consistent Hartree-Fock study of the equation of state (EOS) of asymmetric nuclear matter at finite temperature has been performed using realistic choices of the effective, density-dependent nucleon-nucleon (NN ) interaction, which were successfully used in different nuclear structure and reaction studies. Given the importance of the nuclear symmetry energy in the neutron star formation, EOSs associated with different behaviors of the symmetry energy were used to study hot asymmetric nuclear matter. The slope of the symmetry energy and nucleon effective mass with increasing baryon density was found to affect the thermal properties of nuclear matter significantly. Different density-dependent NN interactions were further used to study the EOS of hot protoneutron star (PNS) matter of the n p e μ ν composition in β equilibrium. The hydrostatic configurations of PNS in terms of the maximal gravitational mass Mmax and radius, central density, pressure, and temperature at the total entropy per baryon S /A =1 ,2 , and 4 have been determined in both the neutrino-free and neutrino-trapped scenarios. The obtained results show consistently a strong impact of the symmetry energy and nucleon effective mass on thermal properties and composition of hot PNS matter. Mmax values obtained for the (neutrino-free) β -stable PNS at S /A =4 were used to assess time tBH of the collapse of a 40 M⊙ protoneutron progenitor to a black hole, based on a correlation between tBH and Mmax found from the hydrodynamic simulation by Hempel et al. [Astrophys. J. 748, 70 (2012), 10.1088/0004-637X/748/1/70].

Reorientation-effect measurement of the first 2+ state in 12C: Confirmation of oblate deformation

NASA Astrophysics Data System (ADS)

Kumar Raju, M.; Orce, J. N.; Navrátil, P.; Ball, G. C.; Drake, T. E.; Triambak, S.; Hackman, G.; Pearson, C. J.; Abrahams, K. J.; Akakpo, E. H.; Al Falou, H.; Churchman, R.; Cross, D. S.; Djongolov, M. K.; Erasmus, N.; Finlay, P.; Garnsworthy, A. B.; Garrett, P. E.; Jenkins, D. G.; Kshetri, R.; Leach, K. G.; Masango, S.; Mavela, D. L.; Mehl, C. V.; Mokgolobotho, M. J.; Ngwetsheni, C.; O'Neill, G. G.; Rand, E. T.; Sjue, S. K. L.; Sumithrarachchi, C. S.; Svensson, C. E.; Tardiff, E. R.; Williams, S. J.; Wong, J.

2018-02-01

A Coulomb-excitation reorientation-effect measurement using the TIGRESS γ-ray spectrometer at the TRIUMF/ISAC II facility has permitted the determination of the 〈 21+ ‖ E 2 ˆ ‖21+ 〉 diagonal matrix element in 12C from particle-γ coincidence data and state-of-the-art no-core shell model calculations of the nuclear polarizability. The nuclear polarizability for the ground and first-excited (21+) states in 12C have been calculated using chiral NN N4LO500 and NN+3NF350 interactions, which show convergence and agreement with photo-absorption cross-section data. Predictions show a change in the nuclear polarizability with a substantial increase between the ground state and first excited 21+ state at 4.439 MeV. The polarizability of the 21+ state is introduced into the current and previous Coulomb-excitation reorientation-effect analyses of 12C. Spectroscopic quadrupole moments of QS (21+) = + 0.053 (44) eb and QS (21+) = + 0.08 (3) eb are determined, respectively, yielding a weighted average of QS (21+) = + 0.071 (25) eb, in agreement with recent ab initio calculations. The present measurement confirms that the 21+ state of 12C is oblate and emphasizes the important role played by the nuclear polarizability in Coulomb-excitation studies of light nuclei.

Quantitative analysis of TALE-DNA interactions suggests polarity effects.

PubMed

Meckler, Joshua F; Bhakta, Mital S; Kim, Moon-Soo; Ovadia, Robert; Habrian, Chris H; Zykovich, Artem; Yu, Abigail; Lockwood, Sarah H; Morbitzer, Robert; Elsäesser, Janett; Lahaye, Thomas; Segal, David J; Baldwin, Enoch P

2013-04-01

Transcription activator-like effectors (TALEs) have revolutionized the field of genome engineering. We present here a systematic assessment of TALE DNA recognition, using quantitative electrophoretic mobility shift assays and reporter gene activation assays. Within TALE proteins, tandem 34-amino acid repeats recognize one base pair each and direct sequence-specific DNA binding through repeat variable di-residues (RVDs). We found that RVD choice can affect affinity by four orders of magnitude, with the relative RVD contribution in the order NG > HD ≈ NN > NI > NK. The NN repeat preferred the base G over A, whereas the NK repeat bound G with 10(3)-fold lower affinity. We compared AvrBs3, a naturally occurring TALE that recognizes its target using some atypical RVD-base combinations, with a designed TALE that precisely matches 'standard' RVDs with the target bases. This comparison revealed unexpected differences in sensitivity to substitutions of the invariant 5'-T. Another surprising observation was that base mismatches at the 5' end of the target site had more disruptive effects on affinity than those at the 3' end, particularly in designed TALEs. These results provide evidence that TALE-DNA recognition exhibits a hitherto un-described polarity effect, in which the N-terminal repeats contribute more to affinity than C-terminal ones.

The South African English Smartphone Digits-in-Noise Hearing Test: Effect of Age, Hearing Loss, and Speaking Competence.

PubMed

Potgieter, Jenni-Marí; Swanepoel, De Wet; Myburgh, Hermanus Carel; Smits, Cas

2017-11-20

This study determined the effect of hearing loss and English-speaking competency on the South African English digits-in-noise hearing test to evaluate its suitability for use across native (N) and non-native (NN) speakers. A prospective cross-sectional cohort study of N and NN English adults with and without sensorineural hearing loss compared pure-tone air conduction thresholds to the speech reception threshold (SRT) recorded with the smartphone digits-in-noise hearing test. A rating scale was used for NN English listeners' self-reported competence in speaking English. This study consisted of 454 adult listeners (164 male, 290 female; range 16 to 90 years), of whom 337 listeners had a best ear four-frequency pure-tone average (4FPTA; 0.5, 1, 2, and 4 kHz) of ≤25 dB HL. A linear regression model identified three predictors of the digits-in-noise SRT, namely, 4FPTA, age, and self-reported English-speaking competence. The NN group with poor self-reported English-speaking competence (≤5/10) performed significantly (p < 0.01) poorer than the N and NN (≥6/10) groups on the digits-in-noise test. Screening characteristics of the test improved with separate cutoff values depending on English-speaking competence for the N and NN groups (≥6/10) and NN group alone (≤5/10). Logistic regression models, which include age in the analysis, showed a further improvement in sensitivity and specificity for both groups (area under the receiver operating characteristic curve, 0.962 and 0.903, respectively). Self-reported English-speaking competence had a significant influence on the SRT obtained with the smartphone digits-in-noise test. A logistic regression approach considering SRT, self-reported English-speaking competence, and age as predictors of best ear 4FPTA >25 dB HL showed that the test can be used as an accurate hearing screening tool for N and NN English speakers. The smartphone digits-in-noise test, therefore, allows testing in a multilingual population familiar with English digits using dynamic cutoff values that can be chosen according to self-reported English-speaking competence and age.

Air Force Health Study. An Epidemiologic Investigation of Health Effects in Air Force Personnel Following Exposure to Herbicides. First Followup Examination Results. Volume 1

DTIC Science & Technology

1987-10-01

Tables 20-1 and 20-2. As shown, nn significant group differtnces were observed for history of asthma, bronchitis, pleurisy , pneumonia, or tuberculosis...sigxaificL, t group-by-pack-year interactions in analyses of a history of pleurisy and tuberculosis, for the presence of rales on examination, and for x-ray... Pleurisy was significantly more frequent in moderately smoking Ranch Hands (p.0.0001), but bordered on being significantly increased in heavily smoking

Evaluating uses of data mining techniques in propensity score estimation: a simulation study.

PubMed

Setoguchi, Soko; Schneeweiss, Sebastian; Brookhart, M Alan; Glynn, Robert J; Cook, E Francis

2008-06-01

In propensity score modeling, it is a standard practice to optimize the prediction of exposure status based on the covariate information. In a simulation study, we examined in what situations analyses based on various types of exposure propensity score (EPS) models using data mining techniques such as recursive partitioning (RP) and neural networks (NN) produce unbiased and/or efficient results. We simulated data for a hypothetical cohort study (n = 2000) with a binary exposure/outcome and 10 binary/continuous covariates with seven scenarios differing by non-linear and/or non-additive associations between exposure and covariates. EPS models used logistic regression (LR) (all possible main effects), RP1 (without pruning), RP2 (with pruning), and NN. We calculated c-statistics (C), standard errors (SE), and bias of exposure-effect estimates from outcome models for the PS-matched dataset. Data mining techniques yielded higher C than LR (mean: NN, 0.86; RPI, 0.79; RP2, 0.72; and LR, 0.76). SE tended to be greater in models with higher C. Overall bias was small for each strategy, although NN estimates tended to be the least biased. C was not correlated with the magnitude of bias (correlation coefficient [COR] = -0.3, p = 0.1) but increased SE (COR = 0.7, p < 0.001). Effect estimates from EPS models by simple LR were generally robust. NN models generally provided the least numerically biased estimates. C was not associated with the magnitude of bias but was with the increased SE.

Atomic force microscopy-guided fractionation reveals the influence of cranberry phytochemicals on adhesion of Escherichia coli.

PubMed

Gupta, Prachi; Song, Biqin; Neto, Catherine; Camesano, Terri A

2016-06-15

Cranberry juice has been long used to prevent infections because of its effect on the adhesion of the bacteria to the host surface. Proanthocyanidins (PACs) comprise of one of the major classes of phytochemicals found in cranberry, which have been extensively studied and found effective in combating adhesion of pathogenic bacteria. The role of other cranberry constituents in impacting bacterial adhesion haven't been studied very well. In this study, cranberry juice fractions were prepared, characterized and tested for their effect on the surface adhesion of the pathogenic clinical bacterial strain E. coli B78 and non-pathogenic control E. coli HB101. The preparations tested included crude cranberry juice extract (CCE); three fractions containing flavonoid classes including proanthocyanidins, anthocyanins and flavonols; selected sub-fractions, and commercially available flavonol glycoside, quercetin-3-O-galactoside. Atomic force microscopy (AFM) was used to quantify the adhesion forces between the bacterial surface and the AFM probe after the treatment with the cranberry fractions. Adhesion forces of the non-pathogenic, non fimbriated lab strain HB101 are small (average force 0.19 nN) and do not change with cranberry treatments, whereas the adhesion forces of the pathogenic, Dr adhesion E. coli strain B78 (average force of 0.42 nN) show a significant decrease when treated with cranberry juice extract or fractions (average force of 0.31 nN, 0.37 nN and 0.39 nN with CCE, Fraction 7 and Fraction 4 respectively). In particular, the fractions that contained flavonols in addition to PACs were more efficient at lowering the force of adhesion (average force of 0.31 nN-0.18 nN between different sub-fractions containing flavonols and PACs). The sub-fractions containing flavonol glycosides (from juice, fruit and commercial quercetin) all resulted in reduced adhesion of the pathogenic bacteria to the model probe. This strongly suggests the anti adhesive role of other classes of cranberry compounds in conjunction with already known PACs and may have implications for development of alternative anti bacterial treatments.

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

PubMed

Jia, Zi-Jun; Song, Yong-Duan

2017-06-01

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

Effect of an InxGa1-xAs-GaAs blocking heterocathode metal contact on the GaAs TED operation

NASA Astrophysics Data System (ADS)

Arkusha, Yu. V.; Prokhorov, E. D.; Storozhenko, I. P.

2004-09-01

The frequency dependence of the generation efficiency of an mm- -nn:In:InxGaGa1-1-xAs- As-nn:GaAs-:GaAs-nn++:GaAs TED with the 2.5-mm long active region is calculated. The optimum values - which yield the diode maximum generation efficiency - for the :GaAs TED with the 2.5-mm long active region is calculated. The optimum values - which yield the diode maximum generation efficiency - for the nn:In:InxGaGa1-1-xAs cathode length, the cathode concentration of ionized impurities, and the height of the potential barrier on metal contact are determined.As cathode length, the cathode concentration of ionized impurities, and the height of the potential barrier on metal contact are determined.

Updated estimates of HAL n and RN- effects on pork quality: fresh and processed loin and ham.

PubMed

Cherel, P; Glénisson, J; Figwer, P; Pires, J; Damon, M; Franck, M; Le Roy, P

2010-12-01

A 1000-pig F2 intercross QTL detection experimental population was generated using two commercial sire lines. Independent carriers of HAL n and RN- mutations (10% and 14%, respectively) were included in this population as control genotypes. The effects of HAL n and RN- heterozygous genotypes on fresh and transformed loins and hams were estimated using a mixed model methodology. The results document the unfavorable effects of both mutations on meat quality. Smaller effects of HAL Nn genotype compared to HAL nn or RN-rn+ genotypes were estimated. Interestingly, effects of HAL Nn genotype on meat pH and loin color could be insignificant at 24-h postmortem, but translate into higher water losses on storage and cooking, and result in tougher cooked loin. Using the same methodology, significant effects of the PRKAG3 (RN) I199 allele on ultimate pH values but not on glycolytic potential were observed. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

Coherent J/ψ photoproduction in ultra-peripheral PbPb collisions at √{sNN} = 2.76TeV with the CMS experiment

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krammer, M.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Heracleous, N.; Keaveney, J.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Van Parijs, I.; Brun, H.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Maerschalk, T.; Marinov, A.; Perniè, L.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Yonamine, R.; Zenoni, F.; Zhang, F.; Benucci, L.; Cimmino, A.; Crucy, S.; Dobur, D.; Fagot, A.; Garcia, G.; Gul, M.; Mccartin, J.; Ocampo Rios, A. A.; Poyraz, D.; Ryckbosch, D.; Salva, S.; Sigamani, M.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; Ceard, L.; De Visscher, S.; Delaere, C.; Delcourt, M.; Favart, D.; Forthomme, L.; Giammanco, A.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Mertens, A.; Musich, M.; Nuttens, C.; Perrini, L.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Beliy, N.; Hammad, G. H.; Aldá, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Correa Martins, M.; Hamer, M.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; De Souza Santos, A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Leggat, D.; Plestina, R.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Micanovic, S.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Finger, M.; Finger, M.; Awad, A.; Elgammal, S.; Mohamed, A.; Salama, E.; Calpas, B.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Peltola, T.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; Filipovic, N.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Miné, P.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Merlin, J. A.; Skovpen, K.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Ruiz Alvarez, J. D.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Bagaturia, I.; Autermann, C.; Beranek, S.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Schael, S.; Schulte, J. F.; Verlage, T.; Weber, H.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Hoehle, F.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Nehrkorn, A.; Nowack, A.; Nugent, I. M.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Asin, I.; Bartosik, N.; Beernaert, K.; Behnke, O.; Behrens, U.; Borras, K.; Burgmeier, A.; Campbell, A.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Dooling, S.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Korol, I.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Nayak, A.; Ntomari, E.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Seitz, C.; Spannagel, S.; Stefaniuk, N.; Trippkewitz, K. D.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Dreyer, T.; Erfle, J.; Garutti, E.; Goebel, K.; Gonzalez, D.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Niedziela, M.; Nowatschin, D.; Ott, J.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Pietsch, N.; Poehlsen, J.; Sander, C.; Scharf, C.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Schumann, S.; Schwandt, J.; Sola, V.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; Colombo, F.; De Boer, W.; Descroix, A.; Dierlamm, A.; Fink, S.; Frensch, F.; Friese, R.; Giffels, M.; Gilbert, A.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Kornmayer, A.; Lobelle Pardo, P.; Maier, B.; Mildner, H.; Mozer, M. U.; Müller, T.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Bartók, M.; Makovec, A.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Choudhury, S.; Mal, P.; Mandal, K.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Keshri, S.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Nishu, N.; Ranjan, K.; Sharma, R.; Sharma, V.; Bhattacharya, R.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutta, S.; Ghosh, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Nandan, S.; Purohit, A.; Roy, A.; Roy, D.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Banerjee, S.; Bhowmik, S.; Chatterjee, R. M.; Dewanjee, R. K.; Dugad, S.; Ganguly, S.; Ghosh, S.; Guchait, M.; Gurtu, A.; Jain, Sa.; Kole, G.; Kumar, S.; Mahakud, B.; Maity, M.; Majumder, G.; Mazumdar, K.; Mitra, S.; Mohanty, G. B.; Parida, B.; Sarkar, T.; Sur, N.; Sutar, B.; Wickramage, N.; Chauhan, S.; Dube, S.; Kapoor, A.; Kothekar, K.; Rane, A.; Sharma, S.; Bakhshiansohi, H.; Behnamian, H.; Etesami, S. M.; Fahim, A.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Venditti, R.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Cappello, G.; Chiorboli, M.; Costa, S.; Di Mattia, A.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Viliani, L.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Primavera, F.; Calvelli, V.; Ferro, F.; Lo Vetere, M.; Monge, M. R.; Robutti, E.; Tosi, S.; Brianza, L.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Malvezzi, S.; Manzoni, R. A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Pigazzini, S.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; Di Guida, S.; Esposito, M.; Fabozzi, F.; Iorio, A. O. M.; Lanza, G.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Bellato, M.; Benato, L.; Boletti, A.; Dall'Osso, M.; Dorigo, T.; Fanzago, F.; Gasparini, F.; Gozzelino, A.; Gulmini, M.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Michelotto, M.; Passaseo, M.; Pazzini, J.; Pegoraro, M.; Pozzobon, N.; Ronchese, P.; Sgaravatto, M.; Simonetto, F.; Torassa, E.; Tosi, M.; Vanini, S.; Ventura, S.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; D'imperio, G.; Del Re, D.; Diemoz, M.; Gelli, S.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bellan, R.; Biino, C.; Cartiglia, N.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; La Licata, C.; Schizzi, A.; Zanetti, A.; Nam, S. K.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Kong, D. J.; Lee, S.; Lee, S. W.; Oh, Y. D.; Sakharov, A.; Son, D. C.; Brochero Cifuentes, J. A.; Kim, H.; Kim, T. J.; Song, S.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Hong, B.; Kim, H.; Kim, Y.; Lee, B.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Yoo, H. D.; Choi, M.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Goh, J.; Kim, D.; Kwon, E.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Komaragiri, J. R.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Yusli, M. N.; Zolkapli, Z.; Casimiro Linares, E.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Vazquez Valencia, F.; Pedraza, I.; Salazar Ibarguen, H. A.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khan, W. A.; Khurshid, T.; Shoaib, M.; Waqas, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Traczyk, P.; Zalewski, P.; Brona, G.; Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Walczak, M.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M. V.; Nguyen, F.; Rodrigues Antunes, J.; Seixas, J.; Toldaiev, O.; Vadruccio, D.; Varela, J.; Vischia, P.; Afanasiev, S.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Voytishin, N.; Zarubin, A.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Vlasov, E.; Zhokin, A.; Chadeeva, M.; Chistov, R.; Danilov, M.; Rusinov, V.; Tarkovskii, E.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Mesyats, G.; Rusakov, S. V.; Baskakov, A.; Belyaev, A.; Boos, E.; Demiyanov, A.; Ershov, A.; Gribushin, A.; Kodolova, O.; Korotkikh, V.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.; Vardanyan, I.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Tourtchanovitch, L.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Cirkovic, P.; Devetak, D.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; de Trocóniz, J. F.; Missiroli, M.; Moran, D.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Palencia Cortezon, E.; Vizan Garcia, J. M.; Cabrillo, I. J.; Calderon, A.; Castiñeiras De Saa, J. R.; Curras, E.; De Castro Manzano, P.; Fernandez, M.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Benhabib, L.; Berruti, G. M.; Bloch, P.; Bocci, A.; Bonato, A.; Botta, C.; Breuker, H.; Camporesi, T.; Castello, R.; Cepeda, M.; Cerminara, G.; D'Alfonso, M.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Guio, F.; De Roeck, A.; Di Marco, E.; Dobson, M.; Dordevic, M.; Dorney, B.; du Pree, T.; Duggan, D.; Dünser, M.; Dupont, N.; Elliott-Peisert, A.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gill, K.; Girone, M.; Glege, F.; Guida, R.; Gundacker, S.; Guthoff, M.; Hammer, J.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kirschenmann, H.; Knünz, V.; Kortelainen, M. J.; Kousouris, K.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Magini, N.; Malgeri, L.; Mannelli, M.; Martelli, A.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Neugebauer, H.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Piparo, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Ruan, M.; Sakulin, H.; Sauvan, J. B.; Schäfer, C.; Schwick, C.; Seidel, M.; Sharma, A.; Silva, P.; Simon, M.; Sphicas, P.; Steggemann, J.; Stoye, M.; Takahashi, Y.; Treille, D.; Triossi, A.; Tsirou, A.; Veckalns, V.; Veres, G. I.; Wardle, N.; Wöhri, H. K.; Zagozdzinska, A.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Casal, B.; Dissertori, G.; Dittmar, M.; Donegà, M.; Eller, P.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Lecomte, P.; Lustermann, W.; Mangano, B.; Marionneau, M.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Rossini, M.; Schönenberger, M.; Starodumov, A.; Takahashi, M.; Tavolaro, V. R.; Theofilatos, K.; Wallny, R.; Aarrestad, T. K.; Amsler, C.; Caminada, L.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Galloni, C.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Ngadiuba, J.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Yang, Y.; Chen, K. H.; Doan, T. H.; Jain, Sh.; Khurana, R.; Konyushikhin, M.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Fiori, F.; Grundler, U.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Miñano Moya, M.; Petrakou, E.; Tsai, J. f.; Tzeng, Y. M.; Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Eskut, E.; Girgis, S.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sunar Cerci, D.; Zorbilmez, C.; Bilin, B.; Bilmis, S.; Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Kaya, M.; Kaya, O.; Yetkin, E. A.; Yetkin, T.; Cakir, A.; Cankocak, K.; Sen, S.; Grynyov, B.; Levchuk, L.; Sorokin, P.; Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. 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M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Lewis, J.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Field, R. D.; Furic, I. K.; Konigsberg, J.; Korytov, A.; Kotov, K.; Ma, P.; Matchev, K.; Mei, H.; Milenovic, P.; Mitselmakher, G.; Rank, D.; Rossin, R.; Shchutska, L.; Snowball, M.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, J. R.; Adams, T.; Askew, A.; Bein, S.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Khatiwada, A.; Prosper, H.; Weinberg, M.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Kalakhety, H.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Kurt, P.; O'Brien, C.; Sandoval Gonzalez, I. D.; Turner, P.; Varelas, N.; Wu, Z.; Zakaria, M.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Anderson, I.; Barnett, B. A.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Osherson, M.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; Xin, Y.; You, C.; Baringer, P.; Bean, A.; Bruner, C.; Castle, J.; Kenny, R. P., III; Kropivnitskaya, A.; Majumder, D.; Malek, M.; Mcbrayer, W.; Murray, M.; Sanders, S.; Stringer, R.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Khalil, S.; Makouski, M.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Lange, D.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Lu, Y.; Mignerey, A. C.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Baty, A.; Bi, R.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; Demiragli, Z.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Krajczar, K.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Sumorok, K.; Tatar, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Benvenuti, A. C.; Dahmes, B.; Evans, A.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bartek, R.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Knowlton, D.; Kravchenko, I.; Meier, F.; Monroy, J.; Ratnikov, F.; Siado, J. E.; Snow, G. R.; Stieger, B.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Zhang, J.; Bhattacharya, S.; Hahn, K. A.; Kubik, A.; Low, J. F.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Rupprecht, N.; Smith, G.; Taroni, S.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Ling, T. Y.; Liu, B.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Zuranski, A.; Malik, S.; Barker, A.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Jung, K.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Chou, J. P.; Contreras-Campana, E.; Ferencek, D.; Gershtein, Y.; Halkiadakis, E.; Heindl, M.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Lath, A.; Nash, K.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Krutelyov, V.; Mueller, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Rathjens, D.; Rose, A.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Wood, J.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Sarangi, T.; Savin, A.; Sharma, A.; Smith, N.; Smith, W. H.; Taylor, D.; Verwilligen, P.; Woods, N.; CMS Collaboration

2017-09-01

The cross section for coherent J / ψ photoproduction accompanied by at least one neutron on one side of the interaction point and no neutron activity on the other side, Xn0n, is measured with the CMS experiment in ultra-peripheral PbPb collisions at √{sNN} = 2.76TeV. The analysis is based on a data sample corresponding to an integrated luminosity of 159μb-1, collected during the 2011 PbPb run. The J / ψ mesons are reconstructed in the dimuon decay channel, while neutrons are detected using zero degree calorimeters. The measured cross section is dσSUB>Xn0n/SUB>coh/dy (J / ψ) = 0.36 ± 0.04(stat) ± 0.04(syst) mb in the rapidity interval 1.8 < | y | < 2.3. Using a model for the relative rate of coherent photoproduction processes, this Xn0n measurement gives a total coherent photoproduction cross section of dσcoh / dy (J / ψ) = 1.82 ± 0.22(stat) ± 0.20(syst) ± 0.19(theo) mb. The data strongly disfavor the impulse approximation model prediction, indicating that nuclear effects are needed to describe coherent J / ψ photoproduction in γ + Pb interactions. The data are found to be consistent with the leading twist approximation, which includes nuclear gluon shadowing.

Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays.

PubMed

Tong, Shao Cheng; Li, Yong Ming; Zhang, Hua-Guang

2011-07-01

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of "explosion of complexity" inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.

Improved biomass and lipid production in Synechocystis sp. NN using industrial wastes and nano-catalyst coupled transesterification for biodiesel production.

PubMed

Jawaharraj, Kalimuthu; Karpagam, Rathinasamy; Ashokkumar, Balasubramaniem; Kathiresan, Shanmugam; Moorthy, Innasi Muthu Ganesh; Arumugam, Muthu; Varalakshmi, Perumal

2017-10-01

In this study, the improved biomass (1.6 folds) and lipid (1.3 folds) productivities in Synechocystis sp. NN using agro-industrial wastes supplementation through hybrid response surface methodology-genetic algorithm (RSM-GA) for cost-effective methodologies for biodiesel production was achieved. Besides, efficient harvesting in Synechocystis sp. NN was achieved by electroflocculation (flocculation efficiency 97.8±1.2%) in 10min when compared to other methods. Furthermore, different pretreatment methods were employed for lipid extraction and maximum lipid content of 19.3±0.2% by Synechocystis sp. NN was attained by ultrasonication than microwave and liquid nitrogen assisted pretreatment methods. The highest FAME (fatty acid methyl ester) conversion of 36.5±8.3mg FAME/g biomass was obtained using titanium oxide as heterogeneous nano-catalyst coupled whole-cell transesterification based method. Conclusively, Synechocystis sp. NN may be used as a biodiesel feedstock and its fuel production can be enriched by hybrid RSM-GA and nano-catalyst technologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

PubMed

Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

2014-07-01

In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Shape-selective recognition of DNA abasic sites by metallohelices: inhibition of human AP endonuclease 1.

PubMed

Malina, Jaroslav; Scott, Peter; Brabec, Viktor

2015-06-23

Loss of a base in DNA leading to creation of an abasic (AP) site leaving a deoxyribose residue in the strand, is a frequent lesion that may occur spontaneously or under the action of various physical and chemical agents. Progress in the understanding of the chemistry and enzymology of abasic DNA largely relies upon the study of AP sites in synthetic duplexes. We report here on interactions of diastereomerically pure metallo-helical 'flexicate' complexes, bimetallic triple-stranded ferro-helicates [Fe2(NN-NN)3](4+) incorporating the common NN-NN bis(bidentate) helicand, with short DNA duplexes containing AP sites in different sequence contexts. The results show that the flexicates bind to AP sites in DNA duplexes in a shape-selective manner. They preferentially bind to AP sites flanked by purines on both sides and their binding is enhanced when a pyrimidine is placed in opposite orientation to the lesion. Notably, the Λ-enantiomer binds to all tested AP sites with higher affinity than the Δ-enantiomer. In addition, the binding of the flexicates to AP sites inhibits the activity of human AP endonuclease 1, which is as a valid anticancer drug target. Hence, this finding indicates the potential of utilizing well-defined metallo-helical complexes for cancer chemotherapy. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Molecular and characterization of NnPPO cDNA from lotus (Nelumbo nucifera) in rhizome browning.

PubMed

Dong, C; Yu, A Q; Yang, M G; Zhou, M Q; Hu, Z L

2016-04-30

The complete cDNA (NnPPO) of polyphenol oxidase in Nelumbo nucifera was successfully isolated, using Rapid amplification cDNA end (RACE) assays. The full-length cDNA of NnPPO was 2069 bp in size, containing a 1791 bp open reading frame coding 597 amino acids. The putative NnPPO possessed the conserved active sites and domains for PPO function. Phylogenetic analysis revealed that NnPPO shared high homology with PPO of high plants, and the homology modeling proved that NnPPO had the typical structure of PPO family. In order to characterize the role of NnPPO, Real-time PCR assay demonstrated that NnPPO mRNA was expressed in different tissues of N. nucifera including young leave, rhizome, flower, root and leafstalk, with the highest expression in rhizome. Patterns of NnPPO expression in rhizome illustrated its mRNA level was significantly elevated, which was consistent with the change of NnPPO activity during rhizome browning. Therefore, transcriptional activation of NnPPO was probably the main reason causing rhizome browning.

40 CFR Table Nn-2 to Subpart Nn of... - Default Values for Calculation Methodology 2 of This Subpart

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) MANDATORY GREENHOUSE GAS REPORTING Suppliers of Natural Gas and Natural Gas Liquids Pt. 98, Subpt. NN, Table NN-2 Table NN-2 to Subpart NN of Part 98.../Unit) 1 Natural Gas Mscf 0.0544 Propane Barrel 0.241 Normal butane Barrel 0.281 Ethane Barrel 0.170...

Neutron knockout from 68,70Ni ground and isomeric states.

NASA Astrophysics Data System (ADS)

Recchia, F.; Weisshaar, D.; Gade, A.; Tostevin, J. A.; Janssens, R. V. F.; Albers, M.; Bader, V. M.; Baugher, T.; Bazin, D.; Berryman, J. S.; Brown, B. A.; Campbell, C. M.; Carpenter, M. P.; Chen, J.; Chiara, C. J.; Crawford, H. L.; Hoffman, C. R.; Kondev, F. G.; Korichi, A.; Langer, C.; Lauritsen, T.; Liddick, S. N.; Lunderberg, E.; Noji, S.; Prokop, C.; Stroberg, S. R.; Suchyta, S.; Wimmer, K.; Zhu, S.

2018-02-01

Neutron-rich isotopes are an important source of new information on nuclear physics. Specifically, the spin-isospin components in the nucleon-nucleon (NN) interaction, e.g., the proton-neutron tensor force, are expected to modify shell structure in exotic nuclei. These potential changes in the intrinsic shell structure are of fundamental interest. The study of the excitation energy of states corresponding to specific configurations in even-even isotopes, together with the single-particle character of the first excited states of odd-A, neutron-rich Ni isotopes, probes the evolution of the neutron orbitals around the Fermi surface as a function of the neutron number a step forward in the understanding of the region and the nature of the NN interaction at large N/Z ratios. In an experiment carried out at the National Superconducting Cyclotron Laboratory [1], new spectroscopic information was obtained for 68Ni and the distribution of single-particle strengths in 67,69Ni was characterized by means of single-neutron knockout from 68,70Ni secondary beams. The spectroscopic strengths, deduced from the measured partial cross sections to the individual states tagged by their de-exciting gamma rays, is used to identify and quantify configurations that involve neutron excitations across the N = 40 harmonic oscillator shell closure. The de-excitation γ rays were measured with the GRETINA tracking array [2]. The results challenge the validity of the most current shell-model Hamiltonians and effective interactions, highlighting shortcomings that cannot yet be explained. These results suggest that our understanding of the low-energy states in such nuclei is not complete and requires further investigation.

Effects of smoking cessation on heart rate variability among long-term male smokers.

PubMed

Harte, Christopher B; Meston, Cindy M

2014-04-01

Cigarette smoking has been shown to adversely affect heart rate variability (HRV), suggesting dysregulation of cardiac autonomic function. Conversely, smoking cessation is posited to improve cardiac regulation. The aim of the present study was to examine the effects of smoking cessation on HRV among a community sample of chronic smokers. Sixty-two healthy male smokers enrolled in an 8-week smoking cessation program involving a nicotine transdermal patch treatment. Participants were assessed at baseline (while smoking regularly), at mid-treatment (while using a high-dose patch), and at follow-up, 4 weeks after patch discontinuation. Both time-domain (standard deviation of normal-to-normal (NN) intervals (SDNN), square root of the mean squared difference of successive NN intervals (RMSSD), and percent of NN intervals for which successive heartbeat intervals differed by at least 50 ms (pNN50)) and frequency-domain (low frequency (LF), high frequency (HF), LF/HF ratio) parameters of HRV were assessed at each visit. Successful quitters (n = 20), compared to those who relapsed (n = 42), displayed significantly higher SDNN, RMSSD, pNN50, LF, and HF at follow-up, when both nicotine and smoke free. Smoking cessation significantly enhances HRV in chronic male smokers, indicating improved autonomic modulation of the heart. Results suggest that these findings may be primarily attributable to nicotine discontinuation rather than tobacco smoke discontinuation alone.

Microscopic significance of hydrophobic residues in the protein-stabilizing effect of trimethylamine N-oxide (TMAO).

PubMed

Yang, Yanmei; Mu, Yuguang; Li, Weifeng

2016-08-10

Although it is widely known that trimethylamine N-oxide (TMAO) stabilizes the native structure of proteins, the underlying mechanism of its action is poorly documented. To obtain an in-depth understanding of this important osmolyte molecule, we conducted large-scale molecular dynamic simulations of model proteins, namely, wild-type villin headpiece protein HP35 and its doubly norleucine-substituent mutant (Lys24/29Nle) HP35NN in pure urea and urea + TMAO mixed solutions for direct comparison. From extensive sampling, the protective capability of TMAO was well captured in the simulations, where HP35NN demonstrated a significantly more stable native structure than HP35 in the presence of TMAO, whereas in pure urea solution, the former denatured in a shorter time. These findings highlight the importance of the two norleucine residues that regulates the interactions of proteins with urea and TMAO. By accessing the hydration and conformational dynamics of both proteins, we were able to directly probe how TMAO compensates the denaturing effect of urea at the atomic level. The accumulation of urea around hydrophobic residues is clearly suppressed, which indicates that the van der Waals interactions between urea and proteins are weakened by TMAO. As a consequence, the hydrophobic core of protein is preferentially protected by TMAO against urea attack. Although the hydrogen bonds (H-bonds) between proteins and urea are suppressed by TMAO, this plays a very minor role than expected in the enhanced protein stability. In addition, TMAO was found to be always excluded from the protein surface and incapable of forming H-bonds with proteins. Thus, the present study provides solid evidence to support the indirect mechanism of TMAO counteracting the denaturing effects of urea.

40 CFR Table Nn-1 to Subpart Nn of... - Default Factors for Calculation Methodology 1 of This Subpart

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) MANDATORY GREENHOUSE GAS REPORTING Suppliers of Natural Gas and Natural Gas Liquids Pt. 98, Subpt. NN, Table NN-1 Table NN-1 to Subpart NN of Part 98... CO2emission factor (kg CO2/MMBtu) Natural Gas 1.026 MMBtu/Mscf 53.06 Propane 3.84 MMBtu/bbl 62.87 Normal...

High-quality two-nucleon potentials up to fifth order of the chiral expansion

NASA Astrophysics Data System (ADS)

Entem, D. R.; Machleidt, R.; Nosyk, Y.

2017-08-01

We present NN potentials through five orders of chiral effective field theory ranging from leading order (LO) to next-to-next-to-next-to-next-to-leading order (N4LO ). The construction may be perceived as consistent in the sense that the same power counting scheme as well as the same cutoff procedures are applied in all orders. Moreover, the long-range parts of these potentials are fixed by the very accurate π N low-energy constants (LECs) as determined in the Roy-Steiner equations analysis by Hoferichter, Ruiz de Elvira, and coworkers. In fact, the uncertainties of these LECs are so small that a variation within the errors leads to effects that are essentially negligible, reducing the error budget of predictions considerably. The NN potentials are fit to the world NN data below the pion-production threshold of the year 2016. The potential of the highest order (N4LO ) reproduces the world NN data with the outstanding χ2/datum of 1.15, which is the highest precision ever accomplished for any chiral NN potential to date. The NN potentials presented may serve as a solid basis for systematic ab initio calculations of nuclear structure and reactions that allow for a comprehensive error analysis. In particular, the consistent order by order development of the potentials will make possible a reliable determination of the truncation error at each order. Our family of potentials is nonlocal and, generally, of soft character. This feature is reflected in the fact that the predictions for the triton binding energy (from two-body forces only) converges to about 8.1 MeV at the highest orders. This leaves room for three-nucleon-force contributions of moderate size.

Heteroleptic copper(I) complexes prepared from phenanthroline and bis-phosphine ligands.

PubMed

Kaeser, Adrien; Mohankumar, Meera; Mohanraj, John; Monti, Filippo; Holler, Michel; Cid, Juan-José; Moudam, Omar; Nierengarten, Iwona; Karmazin-Brelot, Lydia; Duhayon, Carine; Delavaux-Nicot, Béatrice; Armaroli, Nicola; Nierengarten, Jean-François

2013-10-21

Preparation of [Cu(NN)(PP)](+) derivatives has been systematically investigated starting from two libraries of phenanthroline (NN) derivatives and bis-phosphine (PP) ligands, namely, (A) 1,10-phenanthroline (phen), neocuproine (2,9-dimethyl-1,10-phenanthroline, dmp), bathophenanthroline (4,7-diphenyl-1,10-phenanthroline, Bphen), 2,9-diphenethyl-1,10-phenanthroline (dpep), and 2,9-diphenyl-1,10-phenanthroline (dpp); (B) bis(diphenylphosphino)methane (dppm), 1,2-bis(diphenylphosphino)ethane (dppe), 1,3-bis(diphenylphosphino)propane (dppp), 1,2-bis(diphenylphosphino)benzene (dppb), 1,1'-bis(diphenylphosphino)ferrocene (dppFc), and bis[(2-diphenylphosphino)phenyl] ether (POP). Whatever the bis-phosphine ligand, stable heteroleptic [Cu(NN)(PP)](+) complexes are obtained from the 2,9-unsubstituted-1,10-phenanthroline ligands (phen and Bphen). By contrast, heteroleptic complexes obtained from dmp and dpep are stable in the solid state, but a dynamic ligand exchange reaction is systematically observed in solution, and the homoleptic/heteroleptic ratio is highly dependent on the bis-phosphine ligand. Detailed analysis revealed that the dynamic equilibrium resulting from ligand exchange reactions is mainly influenced by the relative thermodynamic stability of the different possible complexes. Finally, in the case of dpp, only homoleptic complexes were obtained whatever the bis-phosphine ligand. Obviously, steric effects resulting from the presence of the bulky phenyl rings on the dpp ligand destabilize the heteroleptic [Cu(NN)(PP)](+) complexes. In addition to the remarkable thermodynamic stability of [Cu(dpp)2]BF4, this negative steric effect drives the dynamic complexation scenario toward almost exclusive formation of homoleptic [Cu(NN)2](+) and [Cu(PP)2](+) complexes. This work provides the definitive rationalization of the stability of [Cu(NN)(PP)](+) complexes, marking the way for future developments in this field.

K(892)* resonance production in Au+Au and p+p collisions at {radical}s{sub NN} = 200 GeV at RHIC

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

Adams, J.; Aggarwal, M.M.; Ahammed, Z.

2004-12-09

The short-lived K(892)* resonance provides an efficient tool to probe properties of the hot and dense medium produced in relativistic heavy-ion collisions. We report measurements of K* in {radical}s{sub NN} = 200 GeV Au+Au and p+p collisions reconstructed via its hadronic decay channels K(892)*{sup 0} {yields} K{pi} and K(892)*{sup +-} {yields} K{sub S}{sup 0}{pi}{sup +-} using the STAR detector at RHIC. The K*{sup 0} mass has been studied as function of p{sub T} in minimum bias p + p and central Au+Au collisions. The K* p{sub T} spectra for minimum bias p + p interactions and for Au+Au collisions inmore » different centralities are presented. The K*/K ratios for all centralities in Au+Au collisions are found to be significantly lower than the ratio in minimum bias p + p collisions, indicating the importance of hadronic interactions between chemical and kinetic freeze-outs. The nuclear modification factor of K* at intermediate p{sub T} is similar to that of K{sub S}{sup 0}, but different from {Lambda}. This establishes a baryon-meson effect over a mass effect in the particle production at intermediate p{sub T} (2 < p{sub T} {le} 4 GeV/c). A significant non-zero K*{sup 0} elliptic flow (v{sub 2}) is observed in Au+Au collisions and compared to the K{sub S}{sup 0} and {Lambda} v{sub 2}.« less

Open Charm Yields in d+Au Collisions at sqrt(sNN) = 200 GeV

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

Adams, J.; Aggarwal, M.M.; Ahammed, Z.

2005-01-07

Mid-rapidity open charm spectra from direct reconstruction of D{sup 0}({bar D}{sup 0}) {yields} K{sup {-+}} {pi}{sup {+-}} in d+Au collisions and indirect electron/positron measurements via charm semileptonic decays in p+p and d+Au collisions at {radical}s{sub NN} = 200 GeV are reported. The D{sup 0}({bar D}{sup 0}) spectrum covers a transverse momentum (p{sub T}) range of 0.1 < p{sub T} < 3 GeV/c whereas the electron spectra cover a range of 1 < p{sub T} < 4 GeV/c. The electron spectra show approximate binary collision scaling between p+p and d+Au collisions. From these two independent analyses, the differential cross section permore » nucleon-nucleon binary interaction at mid-rapidity for open charm production from d+Au collisions at RHIC is d{sigma}{sub c{bar c}}{sup NN}/dy = 0.30 {+-} 0.04 (stat.) {+-} 0.09(syst.) mb. The results are compared to theoretical calculations. Implications for charmonium results in A+A collisions are discussed.« less

Optical pattern recognition algorithms on neural-logic equivalent models and demonstration of their prospects and possible implementations

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Zaitsev, Alexandr V.; Voloshin, Victor M.

2001-03-01

Historic information regarding the appearance and creation of fundamentals of algebra-logical apparatus-`equivalental algebra' for description of neuro-nets paradigms and algorithms is considered which is unification of theory of neuron nets (NN), linear algebra and the most generalized neuro-biology extended for matrix case. A survey is given of `equivalental models' of neuron nets and associative memory is suggested new, modified matrix-tenzor neurological equivalental models (MTNLEMS) are offered with double adaptive-equivalental weighing (DAEW) for spatial-non- invariant recognition (SNIR) and space-invariant recognition (SIR) of 2D images (patterns). It is shown, that MTNLEMS DAEW are the most generalized, they can describe the processes in NN both within the frames of known paradigms and within new `equivalental' paradigm of non-interaction type, and the computing process in NN under using the offered MTNLEMs DAEW is reduced to two-step and multi-step algorithms and step-by-step matrix-tenzor procedures (for SNIR) and procedures of defining of space-dependent equivalental functions from two images (for SIR).

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Hard Break-Up of Two-Nucleons and QCD Dynamics of NN Interaction

NASA Astrophysics Data System (ADS)

Sargsian, Misak

2008-10-01

We discus recent developments in theory of high energy two-body break-up of few-nucleon systems. The characteristics of these reactions are such that the hard two-body quasielastic subprocess can be clearly separated from the accompanying soft subprocesses. We discuss in details the hard rescattering model (HRM) in which hard photodisintegration develops in two stages. At first, photon knocks-out an energetic quark which rescatters subsequently with a quark of the other nucleon. The latter provides a mechanism of sharing the initial high momentum of the photon between two outgoing nucleons. This final state hard rescattering can be expressed through the hard NN scattering amplitude. Within HRM we discuss hard break-up reactions involving D and 3He targets and demonstrate how these reactions are sensitive to the dynamics of hard pn and pp interaction. Another development of HRM is the prediction of new helicity selection mechanism for hard two-body reactions, which was apparently confirmed in the recent JLab experiment.

Output feedback control of a quadrotor UAV using neural networks.

PubMed

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Numerical Exact Ab Initio Four-Nucleon Scattering Calculations: from Dream to Reality

NASA Astrophysics Data System (ADS)

Fonseca, A. C.; Deltuva, A.

2017-03-01

In the present manuscript we review the work of the last ten years on the pursuit to obtain numerical exact solutions of the four-nucleon scattering problem using the most advanced force models that fit two nucleon data up to pion production threshold with a χ ^2 per data point approximately one, together with the Coulomb interaction between protons; three- and four-nucleon forces are also included in the framework of a meson exchange potential model where NN couples to NΔ. Failure to describe the world data on four-nucleon scattering observables in the framework of a non relativistic scattering approach falls necessarily on the force models one uses. Four-nucleon observables pose very clear challenges, particular in the low energy region where there are a number of resonances whose position and width needs to be dynamically generated by the nucleon-nucleon (NN) interactions one uses. In addition, our calculations constitute the most advance piece of work where observables for all four-nucleon reactions involving isospin I=0, I=0 coupled to I=1 and isospin I=1 initial states are calculated at energies both below and above breakup threshold. We also present a very extensive comparison between calculated results and data for cross sections and spin observables. Therefore the present work reveals both the shortcomings and successes of some of the present NN force models in describing four-nucleon data and serve as a benchmark for future developments.

Differential processing of pro-neurotensin/neuromedin N and relationship to pro-hormone convertases.

PubMed

Kitabgi, Patrick

2006-10-01

Neurotensin (NT) is synthesized as part of a larger precursor that also contains neuromedin N (NN), a six amino acid neurotensin-like peptide. NT and NN are located in the C-terminal region of the precursor (pro-NT/NN) where they are flanked and separated by three Lys-Arg sequences. A fourth dibasic sequence is present in the middle of the precursor. Dibasics are the consensus sites recognized and cleaved by endoproteases that belong to the recently identified family of pro-protein convertases (PCs). In tissues that express pro-NT/NN, the three C-terminal Lys-Arg sites are differentially processed, whereas the middle dibasic is poorly cleaved. Pro-NT/NN processing gives rise mainly to NT and NN in the brain, to NT and a large peptide ending with the NN sequence at its C-terminus (large NN) in the gut and to NT, large NN and a large peptide ending with the NT sequence (large NT) in the adrenals. Recent evidence indicates that PC1, PC2 and PC5-A are the pro-hormone convertases responsible for the processing patterns observed in the gut, brain and adrenals, respectively. As NT, NN, large NT and large NN are all endowed with biological activity, the evidence reviewed here supports the idea that post-translational processing of pro-NT/NN in tissues may generate biological diversity.

Resonant Versus Anti-Resonant Tunneling at Carbon Nanotube A-B-A Heterostructures

NASA Technical Reports Server (NTRS)

Mingo, N.; Yang, Liu; Han, Jie; Anantram, M. P.

2001-01-01

Narrow antiresonances going to zero transmission are found to occur for general (2n,0)(n,n)(2n,0) carbon nanotube heterostructures, whereas the complementary configuration, (n,n)(2n,0)(n,n), displays simple resonant tunneling behaviour. We compute examples for different cases, and give a simple explanation for the appearance of antiresonances in one case but not in the other. Conditions and ranges for the occurrence of these different behaviors are stated. The phenomenon of anti-resonant tunneling, which has passed unnoticed in previous studies of nanotube heterostructures, adds up to the rich set of behaviors available to nanotube based quantum effect devices.

Nearest unlike neighbor (NUN): an aid to decision confidence estimation

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1995-09-01

The concept of nearest unlike neighbor (NUN), proposed and explored previously in the design of nearest neighbor (NN) based decision systems, is further exploited in this study to develop a measure of confidence in the decisions made by NN-based decision systems. This measure of confidence, on the basis of comparison with a user-defined threshold, may be used to determine the acceptability of the decision provided by the NN-based decision system. The concepts, associated methodology, and some illustrative numerical examples using the now classical Iris data to bring out the ease of implementation and effectiveness of the proposed innovations are presented.

SUMMARY OF PROGRESS REPORT ON NUCLEAR PHYSICS , DURING YEARLY PERIOD ENDING IN FEBRUARY 1963

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

None

1963-10-31

Aspects of nucleon-nucleon interactions are being examined. Tests of the mathematical form of the one-pion exchange interaction and of the validity of charge independence were improved. No real negation of the applicability of either the mathematical form of the one-pion exchange potential or of charge independence has been found. Work on p-p and p-n scattering analysis centered on improving the accuracy of data and on rewriting IBM 704 programs for the IBM 709 and 7090. The new programs provide separate treatments of errors in quantities such as differential cross sections and in relative values at the same incident energy. Improvementsmore » in the least squares method of adjustment to data were also made. Difficulties in correcting for the effects of nuclear magnetic moments were examined, and formulas and numbers giving the corrections applicable to the one-pion exchange group were worked out and incorporated in new machine programs. A partial analysis of data for n-p scattering in the 2 to 3 Bev energy region shows the existence of a sharp peak in the angular distribution of recoil protons from elastic collisions. Interpretation of work on the interplay of multipion resonance effects with each other and with the direct pion interaction was begun. Systematization of spin rotation effect and aspects of relativistic effects in the Coulomb interaction in p-p scattering was carried out. Treatment of N-N spin- orbit and central field interactions caused by vector meson exchange was improved. Programming of the IBM 709 for computation of the first order nucleonnucleus optical potential and nucleon-nucleus observables in first Born approximation at forward angles from N-N phase parameters was performed. An IBM 709 program for computing pi -N scattering observables from phase shifts was also written. Investigation of photodisintegration of deuterons involved correcting the formulas used in calculating higher order magnetic multipole effects for some omitted effects and rewriting the 704 programs for the IBM 709. Progress in a scattering matrix approach to deuteron photodisintegration was also made. In theory of nucleon-transfer reactions, systematization of the connection between completely quantum mechanical and semiclassical treatments, including analysis of approximations required in the former to obtain the latter, was attempted. Presence of interference effects between the waves was partially confirmed by experiment. Calculations of third order effects in Coulomb excitation were carried out, and programming for more extensive work was begun. Work on interpretation of heavy ion elastic scattering involved additional calculations of the interaction using nucleonnucleus scattering with some calculations using an ingoing wave boundary condition instead of an optical model treatment of the nuclear interior. Twenty-one papers published or submitted for publication during the period are listed. (D.C.W.)« less

Nuclear Matter Effects on ϕ Production in Cu+Au Collisions at √{s}NN = 200 GeV with the PHENIX Muon Arms at RHIC

NASA Astrophysics Data System (ADS)

Jezghani, Margaret; Phenix Collaboration

2015-10-01

A major objective in the field of high-energy nuclear physics is to quantify and characterize the quark-gluon plasma formed in relativistic heavy-ion collisions. The ϕ meson is an excellent probe for studying this hot and dense state of nuclear matter due to its very short lifetime, and the absence of strong interactions between muons and the surrounding hot hadronic matter makes the ϕ to dimuon decay channel particularly interesting. Since the ϕ meson is composed of a strange and antistrange quark, its nuclear modification in heavy-ion collisions may provide insight on strangeness enhancement in-medium. Additionally, the rapidity dependence of ϕ production in asymmetric heavy-ion collisions provides a unique means to study the entanglement of hot and cold nuclear matter effects. In this talk, we present the measurement of ϕ meson production and nuclear modification in asymmetric Cu+Au heavy-ion collisions at √{s}NN = 200 GeV at both forward (Cu-going direction) and backward (Au-going direction) rapidities. This material is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) award program.

Development of an Interactive Computer Program to Produce Body Description Data

DTIC Science & Technology

1983-07-01

arbitrary and has varied over the time that the CVS Program and the ATB Model have been in existence. Program GOOD produces data describing an upper torso...N NN NfU NJ JANNJ NN N5~SA NJN N a~mn ain itn ft atK 0 ,0 9a fK C ca I n k0 rC 91 01 tol s 6, -Inb v v P w Dvf 4oa 0 0 0 IS t. faa 0 o In - v - allT...NAMES/ SUSTYP(4),-SEGLAB(1 5)*J.JTLA9C¶14),PLTSY4!(29), 014NIN(-1 :31 )PTTLEPUNlITS( 3,-1:1) REAL MEAN(C:lp2:3)p STDEVCO:lp2: 3) CHARACTER SU83TYP*20

Disappearance of back-to-back high-pT hadron correlations in central Au+Au collisions at sqrt[s NN ] =200 GeV.

PubMed

Adler, C; Ahammed, Z; Allgower, C; Amonett, J; Anderson, B D; Anderson, M; Averichev, G S; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellwied, R; Berger, J; Bichsel, H; Billmeier, A; Bland, L C; Blyth, C O; Bonner, B E; Boucham, A; Brandin, A; Bravar, A; Cadman, R V; Caines, H; Calderón de la Barca Sánchez, M; Cardenas, A; Carroll, J; Castillo, J; Castro, M; Cebra, D; Chaloupka, P; Chattopadhyay, S; Chen, Y; Chernenko, S P; Cherney, M; Chikanian, A; Choi, B; Christie, W; Coffin, J P; Cormier, T M; Corral, M M; Cramer, J G; Crawford, H J; Derevschikov, A A; Didenko, L; Dietel, T; Draper, J E; Dunin, V B; Dunlop, J C; Eckardt, V; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Fachini, P; Faine, V; Faivre, J; Fatemi, R; Filimonov, K; Finch, E; Fisyak, Y; Flierl, D; Foley, K J; Fu, J; Gagliardi, C A; Gagunashvili, N; Gans, J; Gaudichet, L; Germain, M; Geurts, F; Ghazikhanian, V; Grachov, O; Grigoriev, V; Guedon, M; Gushin, E; Hallman, T J; Hardtke, D; Harris, J W; Henry, T W; Heppelmann, S; Herston, T; Hippolyte, B; Hirsch, A; Hjort, E; Hoffmann, G W; Horsley, M; Huang, H Z; Humanic, T J; Igo, G; Ishihara, A; Ivanshin, Yu I; Jacobs, P; Jacobs, W W; Janik, M; Johnson, I; Jones, P G; Judd, E G; Kaneta, M; Kaplan, M; Keane, D; Kiryluk, J; Kisiel, A; Klay, J; Klein, S R; Klyachko, A; Kollegger, T; Konstantinov, A S; Kopytine, M; Kotchenda, L; Kovalenko, A D; Kramer, M; Kravtsov, P; Krueger, K; Kuhn, C; Kulikov, A I; Kunde, G J; Kunz, C L; Kutuev, R Kh; Kuznetsov, A A; Lakehal-Ayat, L; Lamont, M A C; Landgraf, J M; Lange, S; Lansdell, C P; Lasiuk, B; Laue, F; Lauret, J; Lebedev, A; Lednický, R; Leontiev, V M; LeVine, M J; Li, Q; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, L; Liu, Z; Liu, Q J; Ljubicic, T; Llope, W J; LoCurto, G; Long, H; Longacre, R S; Lopez-Noriega, M; Love, W A; Ludlam, T; Lynn, D; Ma, J; Magestro, D; Majka, R; Margetis, S; Markert, C; Martin, L; Marx, J; Matis, H S; Matulenko, Yu A; McShane, T S; Meissner, F; Melnick, Yu; Meschanin, A; Messer, M; Miller, M L; Milosevich, Z; Minaev, N G; Mitchell, J; Moore, C F; Morozov, V; de Moura, M M; Munhoz, M G; Nelson, J M; Nevski, P; Nikitin, V A; Nogach, L V; Norman, B; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Paic, G; Pandey, S U; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Perevoztchikov, V; Peryt, W; Petrov, V A; Planinic, M; Pluta, J; Porile, N; Porter, J; Poskanzer, A M; Potrebenikova, E; Prindle, D; Pruneau, C; Putschke, J; Rai, G; Rakness, G; Ravel, O; Ray, R L; Razin, S V; Reichhold, D; Reid, J G; Renault, G; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevski, O V; Romero, J L; Rose, A; Roy, C; Rykov, V; Sakrejda, I; Salur, S; Sandweiss, J; Savin, I; Schambach, J; Scharenberg, R P; Schmitz, N; Schroeder, L S; Schüttauf, A; Schweda, K; Seger, J; Seliverstov, D; Seyboth, P; Shahaliev, E; Shestermanov, K E; Shimanskii, S S; Simon, F; Skoro, G; Smirnov, N; Snellings, R; Sorensen, P; Sowinski, J; Spinka, H M; Srivastava, B; Stephenson, E J; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Struck, C; Suaide, A A P; Sugarbaker, E; Suire, C; Sumbera, M; Surrow, B; Symons, T J M; Szanto de Toledo, A; Szarwas, P; Tai, A; Takahashi, J; Tang, A H; Thein, D; Thomas, J H; Thompson, M; Tikhomirov, V; Tokarev, M; Tonjes, M B; Trainor, T A; Trentalange, S; Tribble, R E; Trofimov, V; Tsai, O; Ullrich, T; Underwood, D G; Van Buren, G; VanderMolen, A M; Vasilevski, I M; Vasiliev, A N; Vigdor, S E; Voloshin, S A; Wang, F; Ward, H; Watson, J W; Wells, R; Westfall, G D; Whitten, C; Wieman, H; Willson, R; Wissink, S W; Witt, R; Wood, J; Xu, N; Xu, Z; Yakutin, A E; Yamamoto, E; Yang, J; Yepes, P; Yurevich, V I; Zanevski, Y V; Zborovský, I; Zhang, H; Zhang, W M; Zoulkarneev, R; Zubarev, A N

2003-02-28

Azimuthal correlations for large transverse momentum charged hadrons have been measured over a wide pseudorapidity range and full azimuth in Au+Au and p+p collisions at sqrt[s(NN)]=200 GeV. The small-angle correlations observed in p+p collisions and at all centralities of Au+Au collisions are characteristic of hard-scattering processes previously observed in high-energy collisions. A strong back-to-back correlation exists for p+p and peripheral Au+Au. In contrast, the back-to-back correlations are reduced considerably in the most central Au+Au collisions, indicating substantial interaction as the hard-scattered partons or their fragmentation products traverse the medium.

Study of Omega-proton correlations in heavy-ion collisions

NASA Astrophysics Data System (ADS)

Han, Yifei; STAR Collaboration

2015-10-01

Recently the STAR experiment at RHIC measured Lambda-Lambda correlations from Au+Au collisions at √{sNN} = 200 GeV to search for the H particle (uuddss). The correlation strength indicated that the Lambda-Lambda interaction is weak and is unlikely to be attractive enough to form a bound state. A recent lattice QCD calculation predicted a possible di-baryon bound state with Omega-nucleon. Thus, we will extend the correlation measurements to Omega-proton, which could potentially be a sensitive approach to search for such a state. We will present the Omega-proton correlations based on data collected by STAR in Au+Au collisions at √{sNN} =200 GeV, and discuss the physics implications. for the STAR collaboration.

Excess of J/ψ Production at Very Low Transverse Momenta in A+A Collisions from STAR

NASA Astrophysics Data System (ADS)

Zha, Wangmei

A significant excess of J/ψ yield at very low transverse momentum (pT < 0.3 GeV/c) has been observed by the ALICE Collaboration in peripheral Pb+Pb collisions, which points to evidence of coherent photoproduction of J/ψ in violent hadronic interactions. The survival of photoproduced J/ψ merits more experimental investigations. In this article, we report on the STAR measurements of J/ψ production at very low transverse momenta (pT) in hadronic Au+Au collisions at sNN = 200 GeV and U+U collisions at sNN = 193 GeV at mid-rapidity. Centrality dependence of J/ψ yields and nuclear modification factors at very low pT are presented.

RNA-seq reveals differentially expressed genes of rice (Oryza sativa) spikelet in response to temperature interacting with nitrogen at meiosis stage.

PubMed

Yang, Jun; Chen, Xiaorong; Zhu, Changlan; Peng, Xiaosong; He, Xiaopeng; Fu, Junru; Ouyang, Linjuan; Bian, Jianmin; Hu, Lifang; Sun, Xiaotang; Xu, Jie; He, Haohua

2015-11-17

Rice (Oryza sativa) is one of the most important cereal crops, providing food for more than half of the world's population. However, grain yields are challenged by various abiotic stresses such as drought, fertilizer, heat, and their interaction. Rice at reproductive stage is much more sensitive to environmental temperatures, and little is known about molecular mechanisms of rice spikelet in response to high temperature interacting with nitrogen (N). Here we reported the transcriptional profiling analysis of rice spikelet at meiosis stage using RNA sequencing (RNA-seq) as an attempt to gain insights into molecular events associated with temperature and nitrogen. This study received four treatments: 1) NN: normal nitrogen level (165 kg ha(-1)) with natural temperature (30 °C); 2) HH: high nitrogen level (330 kg ha(-1)) with high temperature (37 °C); 3) NH: normal nitrogen level and high temperature; and 4) HN: high nitrogen level and natural temperature, respectively. The de novo assembly generated 52,553,536 clean reads aligned with 72,667 unigenes. About 10 M reads were identified from each treatment. In these differentially expressed genes (DEGs), we found 151 and 323 temperature-responsive DEGs in NN-vs-NH and HN-vs-HH, and 114 DEGs were co-expressed. Meanwhile, 203 and 144 nitrogen-responsive DEGs were focused in NN-vs-HN and NH-vs-HH, and 111 DEGs were co-expressed. The temperature-responsive genes were principally associated with calcium-dependent protein, cytochrome, flavonoid, heat shock protein, peroxidase, ubiquitin, and transcription factor while the nitrogen-responsive genes were mainly involved in glutamine synthetase, transcription factor, anthocyanin, amino acid transporter, leucine zipper protein, and hormone. It is noted that, rice spikelet fertility was significantly decreased under high temperature, but it was more reduced under higher nitrogen. Accordingly, numerous spikelet genes involved in pollen development, pollen tube growth, pollen germination, especially sporopollenin biosynthetic process, and pollen exine formation were mainly down-regulated under high temperature. Moreover, the expression levels of co-expressed DEGs including 5 sporopollenin biosynthetic process and 7 pollen exine formation genes of NN-vs-NH were lower than that of HN-vs-HH. Therefore, these spikelet genes may play important roles in response to high temperature with high nitrogen and may be good candidates for crop improvement. This RNA-seq study will help elucidate the molecular mechanisms of rice spikelet defense response to high temperature interacting with high nitrogen level.

Identification of Volcanic Landforms and Processes on Earth and Mars using Geospatial Analysis (Invited)

NASA Astrophysics Data System (ADS)

Fagents, S. A.; Hamilton, C. W.

2009-12-01

Nearest neighbor (NN) analysis enables the identification of landforms using non-morphological parameters and can be useful for constraining the geological processes contributing to observed patterns of spatial distribution. Explosive interactions between lava and water can generate volcanic rootless cone (VRC) groups that are well suited to geospatial analyses because they consist of a large number of landforms that share a common formation mechanism. We have applied NN analysis tools to quantitatively compare the spatial distribution of VRCs in the Laki lava flow in Iceland to analogous landforms in the Tartarus Colles Region of eastern Elysium Planitia, Mars. Our results show that rootless eruption sites on both Earth and Mars exhibit systematic variations in spatial organization that are related to variations in the distribution of resources (lava and water) at different scales. Field observations in Iceland reveal that VRC groups are composite structures formed by the emplacement of chronologically and spatially distinct domains. Regionally, rootless cones cluster into groups and domains, but within domains NN distances exhibit random to repelled distributions. This suggests that on regional scales VRCs cluster in locations that contain sufficient resources, whereas on local scales rootless eruption sites tend to self-organize into distributions that maximize the utilization of limited resources (typically groundwater). Within the Laki lava flow, near-surface water is abundant and pre-eruption topography appears to exert the greatest control on both lava inundation regions and clustering of rootless eruption sites. In contrast, lava thickness appears to be the controlling factor in the formation of rootless eruption sites in the Tartarus Colles Region. A critical lava thickness may be required to initiate rootless eruptions on Mars because the lava flows must contain sufficient heat for transferred thermal energy to reach the underlying cryosphere and volatilize buried ground ice. In both environments, the spatial distribution of rootless eruption sites on local scales may either be random, which indicates that rootless eruption sites form independently of one another, or repelled, which implies resource limitation. Where competition for limited groundwater causes rootless eruption sites to develop greater than random NN separation, rootless eruption sites can be modeled as a system of pumping wells that extract water from a shared aquifer, thereby generating repelled distributions due to non-initiation or early cessation of rootless explosive activity at sites with insufficient access to groundwater. Thus statistical NN analyses can be combined with field observations and remote sensing to obtain information about self-organization processes within geological systems and the effects of environmental resource limitation on the spatial distribution of volcanic landforms. NN analyses may also be used to quantitatively compare the spatial distribution of landforms in different planetary environments and for supplying non-morphological evidence to discriminate between feature identities and geological formation mechanisms.

Constraining the magnitude of the Chiral Magnetic Effect with Event Shape Engineering in Pb-Pb collisions at √{sNN } = 2.76 TeV

NASA Astrophysics Data System (ADS)

Acharya, S.; Adam, J.; Adamová, D.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, N.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Al-Turany, M.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altenkamper, L.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andreou, D.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C.; Antičić, T.; Antinori, F.; Antonioli, P.; Anwar, R.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Ball, M.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barioglio, L.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Batigne, G.; Batyunya, B.; Batzing, P. C.; Bazo Alba, J. L.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, A.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Blair, J. T.; Blau, D.; Blume, C.; Boca, G.; Bock, F.; Bogdanov, A.; Boldizsár, L.; Bombara, M.; Bonomi, G.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Botta, E.; Bourjau, C.; Bratrud, L.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Capon, A. A.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cerello, P.; Chandra, S.; Chang, B.; Chapeland, S.; Chartier, M.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Chojnacki, M.; Choudhury, S.; Chowdhury, T.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Concas, M.; Conesa Balbastre, G.; Conesa Del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Costanza, S.; Crkovská, J.; Crochet, P.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; de, S.; de Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; de Falco, A.; de Gruttola, D.; De Marco, N.; de Pasquale, S.; de Souza, R. D.; Degenhardt, H. F.; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; di Bari, D.; di Mauro, A.; di Nezza, P.; di Ruzza, B.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Doremalen, L. V. R.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dukhishyam, M.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Fabbietti, L.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Ganoti, P.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Gay Ducati, M. B.; Germain, M.; Ghosh, J.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Greiner, L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Gronefeld, J. M.; Grosa, F.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Haque, M. R.; Harris, J. W.; Harton, A.; Hassan, H.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Hernandez, E. G.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hills, C.; Hippolyte, B.; Hladky, J.; Hohlweger, B.; Horak, D.; Hornung, S.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Iga Buitron, S. A.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Irfan, M.; Islam, M. S.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovsky, J.; Jaelani, S.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jercic, M.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karczmarczyk, P.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Ketzer, B.; Khabanova, Z.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kielbowicz, M. M.; Kileng, B.; Kim, B.; Kim, D.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Köhler, M. K.; Kollegger, T.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Konyushikhin, M.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Kreis, L.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lai, Y. S.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lavicka, R.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Lévai, P.; Li, X.; Lien, J.; Lietava, R.; Lim, B.; Lindal, S.; Lindenstruth, V.; Lindsay, S. W.; Lippmann, C.; Lisa, M. A.; Litichevskyi, V.; Llope, W. J.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Loncar, P.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Luhder, J. R.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martinez, J. A. L.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Masciocchi, S.; Masera, M.; Masoni, A.; Masson, E.; Mastroserio, A.; Mathis, A. M.; Matuoka, P. F. T.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mihaylov, D. L.; Mikhaylov, K.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Mohisin Khan, M.; Montes, E.; Moreira de Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Myers, C. J.; Myrcha, J. W.; Nag, D.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Narayan, A.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao de Oliveira, R. A.; Nellen, L.; Nesbo, S. V.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Ohlson, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Pachmayer, Y.; Pacik, V.; Pagano, D.; Pagano, P.; Paić, G.; Palni, P.; Pan, J.; Pandey, A. K.; Panebianco, S.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Parmar, S.; Passfeld, A.; Pathak, S. P.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira, L. G.; Pereira da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Pezzi, R. P.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pliquett, F.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Pozdniakov, V.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Rana, D. B.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Rokita, P. S.; Ronchetti, F.; Rosas, E. D.; Rosnet, P.; Rossi, A.; Rotondi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rueda, O. V.; Rui, R.; Rumyantsev, B.; Rustamov, A.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Saha, S. K.; Sahlmuller, B.; Sahoo, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandoval, A.; Sarkar, D.; Sarkar, N.; Sarma, P.; Sas, M. H. P.; Scapparone, E.; Scarlassara, F.; Schaefer, B.; Scharenberg, R. P.; Scheid, H. S.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M. O.; Schmidt, M.; Schmidt, N. V.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sett, P.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shahoyan, R.; Shaikh, W.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silaeva, S.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Stocco, D.; Storetvedt, M. M.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thakur, D.; Thakur, S.; Thomas, D.; Thoresen, F.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Torres, S. R.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Tropp, L.; Trubnikov, V.; Trzaska, W. H.; Trzeciak, B. A.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Umaka, E. N.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; van der Maarel, J.; van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Wagner, B.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wenzel, S. C.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Willsher, E.; Windelband, B.; Witt, W. E.; Yalcin, S.; Yamakawa, K.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.; Zou, S.; Alice Collaboration

2018-02-01

In ultrarelativistic heavy-ion collisions, the event-by-event variation of the elliptic flow v2 reflects fluctuations in the shape of the initial state of the system. This allows to select events with the same centrality but different initial geometry. This selection technique, Event Shape Engineering, has been used in the analysis of charge-dependent two- and three-particle correlations in Pb-Pb collisions at √{sNN } = 2.76 TeV. The two-particle correlator 〈 cos ⁡ (φα -φβ) 〉, calculated for different combinations of charges α and β, is almost independent of v2 (for a given centrality), while the three-particle correlator 〈 cos ⁡ (φα +φβ - 2Ψ2) 〉 scales almost linearly both with the event v2 and charged-particle pseudorapidity density. The charge dependence of the three-particle correlator is often interpreted as evidence for the Chiral Magnetic Effect (CME), a parity violating effect of the strong interaction. However, its measured dependence on v2 points to a large non-CME contribution to the correlator. Comparing the results with Monte Carlo calculations including a magnetic field due to the spectators, the upper limit of the CME signal contribution to the three-particle correlator in the 10-50% centrality interval is found to be 26-33% at 95% confidence level.

Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-09-07

In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation.

PubMed

Yong-Feng Gao; Xi-Ming Sun; Changyun Wen; Wei Wang

2017-07-01

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary system is employed. By the aid of adaptive backstepping technique, an adaptive tracking control approach is developed. Under the proposed adaptive tracking controller, the boundedness of all the signals in the closed-loop system is achieved. Moreover, distinct from most of the existing references, the tracking error can be bounded by an explicit function of design parameters and saturation input error. Finally, an example is given to show the effectiveness of the proposed method.

Effects of analogues of ethanolamine and choline on phospholipid metabolism in rat hepatocytes

PubMed Central

Åkesson, Björn

1977-01-01

1. Analogues of ethanolamine and choline were incubated with different labelled precursors of phospholipids and isolated hepatocytes and the effects on phospholipid synthesis were studied. 2. 2-Aminopropan-1-ol and 2-aminobutan-1-ol were the most efficient inhibitors of [14C]ethanolamine incorporation into phospholipids, whereas the incorporation of [3H]choline was inhibited most extensively by NN-diethylethanolamine and NN-dimethylethanolamine. 3. When the analogues were incubated with [3H]glycerol and hepatocytes, the appearance of 3H in unnatural phospholipids indicated that they were incorporated, at least in part, via CDP-derivatives. The distribution of [3H]glycerol among molecular species of phospholipids containing 2-aminopropan-1-ol and 1-aminopropan-2-ol was the same as in phosphatidylethanolamine. In other phospholipid analogues the distribution of 3H was more similar to that in phosphatidylcholine. 4. NN-Diethylethanolamine stimulated both the conversion of phosphatidylethanolamine into phosphatidylcholine and the incorporation of [Me-14C]methionine into phospholipids. Other N-alkyl- or NN-dialkyl-ethanolamines also stimulated [14C]methionine incorporation, but inhibited the conversion of phosphatidylethanolamine into phosphatidylcholine. This indicates that phosphatidyl-NN-diethylethanolamine is a poor methyl acceptor, in contrast with other N-alkylated phosphatidylethanolamines. 5. These results on the regulation of phospholipid metabolism in intact cells are discussed with respect to the possible control points. They also provide guidelines for future experiments on the manipulation of phospholipid polar-headgroup composition in primary cultures of hepatocytes. PMID:606244

Effects of analogles of ethanolamine and choline on phospholipid metabolism in rat hepatocytes.

PubMed

Akesson, B

1977-12-15

1. Analogues of ethanolamine and choline were incubated with different labelled precursors of phospholipids and isolated hepatocytes and the effects on phospholipid synthesis were studied. 2. 2-Aminopropan-1-ol and 2-aminobutan-1-ol were the most efficient inhibitors of [(14)C]ethanolamine incorporation into phospholipids, whereas the incorporation of [(3)H]choline was inhibited most extensively by NN-diethylethanolamine and NN-dimethylethanolamine. 3. When the analogues were incubated with [(3)H]glycerol and hepatocytes, the appearance of (3)H in unnatural phospholipids indicated that they were incorporated, at least in part, via CDP-derivatives. The distribution of [(3)H]glycerol among molecular species of phospholipids containing 2-aminopropan-1-ol and 1-aminopropan-2-ol was the same as in phosphatidylethanolamine. In other phospholipid analogues the distribution of (3)H was more similar to that in phosphatidylcholine. 4. NN-Diethylethanolamine stimulated both the conversion of phosphatidylethanolamine into phosphatidylcholine and the incorporation of [Me-(14)C]methionine into phospholipids. Other N-alkyl- or NN-dialkyl-ethanolamines also stimulated [(14)C]methionine incorporation, but inhibited the conversion of phosphatidylethanolamine into phosphatidylcholine. This indicates that phosphatidyl-NN-diethylethanolamine is a poor methyl acceptor, in contrast with other N-alkylated phosphatidylethanolamines. 5. These results on the regulation of phospholipid metabolism in intact cells are discussed with respect to the possible control points. They also provide guidelines for future experiments on the manipulation of phospholipid polar-headgroup composition in primary cultures of hepatocytes.

Inverting dynamic force microscopy: From signals to time-resolved interaction forces

PubMed Central

Stark, Martin; Stark, Robert W.; Heckl, Wolfgang M.; Guckenberger, Reinhard

2002-01-01

Transient forces between nanoscale objects on surfaces govern friction, viscous flow, and plastic deformation, occur during manipulation of matter, or mediate the local wetting behavior of thin films. To resolve transient forces on the (sub) microsecond time and nanometer length scale, dynamic atomic force microscopy (AFM) offers largely unexploited potential. Full spectral analysis of the AFM signal completes dynamic AFM. Inverting the signal formation process, we measure the time course of the force effective at the sensing tip. This approach yields rich insight into processes at the tip and dispenses with a priori assumptions about the interaction, as it relies solely on measured data. Force measurements on silicon under ambient conditions demonstrate the distinct signature of the interaction and reveal that peak forces exceeding 200 nN are applied to the sample in a typical imaging situation. These forces are 2 orders of magnitude higher than those in covalent bonds. PMID:12070341

Measurement of electrons from beauty-hadron decays in p-Pb collisions at √{s_{NN}}=5.02 TeV and Pb-Pb collisions at √{s_{NN}}=2.76 TeV

NASA Astrophysics Data System (ADS)

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F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Isakov, V.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lazaridis, L.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Mishra, T.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao De Oliveira, R. A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Palni, P.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira Da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yalcin, S.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.

2017-07-01

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1

Measurement of electrons from beauty-hadron decays in p-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=5.02 $$ TeV and Pb-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=2.76 $$ TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2017-07-11

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1sNN = 5.02 TeV and in 1.3 < pT< 8 GeV/c in the 20% most central Pb-Pb collisions at √ sNN = 2.76 TeV. The pp reference spectra at √ sNN = 5.02 TeV and √s = 2.76 TeV, needed for the calculation of the nuclear modification factors R pPb and R PbPb, were obtained by a pQCD-driven scaling of the cross section of electrons from beauty-hadron decays measured at √s = 7 TeV. In themore » p T interval 3 < p T < 8 GeV/c, a suppression of the yield of electrons from beauty-hadron decays is observed in Pb-Pb compared to pp collisions. Towards lower p T, the R PbPb values increase with large systematic uncertainties. The R pPb is consistent with unity within systematic uncertainties and is well described by theoretical calculations that include cold nuclear matter effects in p-Pb collisions. The measured R pPb and these calculations indicate that cold nuclear matter effects are small at high transverse momentum also in Pb-Pb collisions. Therefore, the observed reduction of R PbPb below unity at high p T may be ascribed to an effect of the hot and dense medium formed in Pb-Pb collisions.« less

Measurement of electrons from beauty-hadron decays in p-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=5.02 $$ TeV and Pb-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=2.76 $$ TeV

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

Adam, J.; Adamová, D.; Aggarwal, M. M.

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1sNN = 5.02 TeV and in 1.3 < pT< 8 GeV/c in the 20% most central Pb-Pb collisions at √ sNN = 2.76 TeV. The pp reference spectra at √ sNN = 5.02 TeV and √s = 2.76 TeV, needed for the calculation of the nuclear modification factors R pPb and R PbPb, were obtained by a pQCD-driven scaling of the cross section of electrons from beauty-hadron decays measured at √s = 7 TeV. In themore » p T interval 3 < p T < 8 GeV/c, a suppression of the yield of electrons from beauty-hadron decays is observed in Pb-Pb compared to pp collisions. Towards lower p T, the R PbPb values increase with large systematic uncertainties. The R pPb is consistent with unity within systematic uncertainties and is well described by theoretical calculations that include cold nuclear matter effects in p-Pb collisions. The measured R pPb and these calculations indicate that cold nuclear matter effects are small at high transverse momentum also in Pb-Pb collisions. Therefore, the observed reduction of R PbPb below unity at high p T may be ascribed to an effect of the hot and dense medium formed in Pb-Pb collisions.« less

Prohormone convertases differentially process pro-neurotensin/neuromedin N in tissues and cell lines.

PubMed

Kitabgi, Patrick

2006-08-01

Neurotensin (NT) is synthesized as part of a larger precursor that also contains neuromedin N (NN), a six-amino acid neurotensin-like peptide. NT and NN are located in the C-terminal region of the precursor (pro-NT/NN) where they are flanked and separated by three Lys-Arg sequences. A fourth dibasic sequence is present in the middle of the precursor. Dibasics are the consensus sites recognized and cleaved by specialized endoproteases that belong to the family of proprotein convertases (PCs). In tissues that express pro-NT/NN, the three C-terminal Lys-Arg sites are differentially processed, whereas the middle dibasic is poorly cleaved. Processing gives rise mainly to NT and NN in the brain, to NT and a large peptide with a C-terminal NN moiety (large NN) in the gut, and to NT, large NN, and a large peptide with a C-terminal NT moiety (large NT) in the adrenals. Recent evidence indicates that PC1, PC2, and PC5-A are the prohormone convertases responsible for the processing patterns observed in the gut, brain, and adrenals, respectively. As NT, NN, large NT, and large NN are all endowed with biological activity, the evidence reviewed in this paper supports the idea that posttranslational processing of pro-NT/NN in tissues may generate biological diversity of pathophysiological relevance.

Neurotensin and neuromedin N are differentially processed from a common precursor by prohormone convertases in tissues and cell lines.

PubMed

Kitabgi, Patrick

2010-01-01

Neurotensin (NT) is synthesized as part of a larger precursor that also contains neuromedin N (NN), a six amino acid NT-like peptide. NT and NN are located in the C-terminal region of the precursor (pro-NT/NN) where they are flanked and separated by three Lys-Arg sequences. A fourth dibasic sequence is present in the middle of the precursor. Dibasics are the consensus sites recognized and cleaved by specialized endoproteases that belong to the family of proprotein convertases (PCs). In tissues that express pro-NT/NN, the three C-terminal Lys-Arg sites are differentially processed, whereas the middle dibasic is poorly cleaved. Processing gives rise mainly to NT and NN in the brain, NT and a large peptide with a C-terminal NN moiety (large NN) in the gut, and NT, large NN, and a large peptide with a C-terminal NT moiety (large NT) in the adrenals. Recent evidence indicates that PC1, PC2, and PC5-A are the prohormone convertases responsible for the processing patterns observed in the gut, brain, and adrenals, respectively. As NT, NN, large NT, and large NN are all endowed with biological activity, the evidence reviewed here supports the idea that posttranslational processing of pro-NT/NN in tissues may generate biological diversity of pathophysiological relevance.

Oxygen reduction reaction catalyzed by platinum nanonetwork prepared by template free one step synthesis for polymer electrolyte membrane fuel cells

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

Narayanamoorthy, B.; Kumar, B.V.V.S. Pavan; Eswaramoorthy, M.

2014-07-01

Highlights: • Supportless Pt nanonetwork (Pt NN) synthesized by novel template free one step method as per our earlier reported procedure. • Electrocatalytic activity of Pt NN studied taking oxygen reduction reaction in acid medium. • Kinetic and thermodynamic parameters were deduced under hydrodynamic conditions. • ORR mechanistic pathway was proposed based on kinetic rate constants. • ADT analysis found enhanced stability (5000 cycles) for Pt NN than Pt NN/VC and reported Pt/C. - Abstract: The reduction reaction of molecular oxygen (ORR) was investigated using supportless Pt nanonetwork (Pt NN) electrocatalyst in sulfuric acid medium. Pt NN was prepared bymore » template free borohydride reduction. The transmission electron microscope images revealed a network like nano-architecture having an average cluster size of 30 nm. The electrochemical characterization of supportless and Vulcan carbon supported Pt NN (Pt NN/VC) was carried out using rotating disc and ring disc electrodes at various temperatures. Kinetic and thermodynamic parameters were estimated under hydrodynamic conditions and compared with Pt NN/VC and reported Pt/C catalysts. The accelerated durability test revealed that supportless Pt NN is quite stable for 5000 potential cycles with 22% reduction in electrochemical surface area (ECSA). While the initial limiting current density has in fact increased by 11.6%, whereas Pt NN/VC suffered nearly 55% loss in ECSA and 13% loss in limiting current density confirming an enhanced stability of supportless Pt NN morphology for ORR compared to conventional Pt/C ORR catalysts in acid medium.« less

Application of the Wavy Mechanical Face Seal to Submarine Seal Design

DTIC Science & Technology

1984-07-01

i2), IIIMRT(±.2i2) 146 COMMON IVMATC12,. 1.),. BMAT (360). RHAi(12),RHB(i𔃼) 150 COMMON RCP, RCM, RF, RS 160 REAL*4 MTM. MTP, JXP, JYP, JX%’PJTP, JXM...NN-I. N 3326 NC-n(NN-l)*12+1 330 OMRTCNC)=BM’IT(NC)eCVXP(NN) 3349 BMAT (NC.I)- BMAT (NC..I)eCYPNN) 3359 IPIRT(NC+5)u=tIAT(NC.S)+CMTP(NN) 3360 OMAT(NC4𔄀... BMAT (NC+6)4CYfl(NN) 3378 SM’AT(NC.7)onBMRT(NC+7)+CYYfl(NN) 3380 700 BflRT(NC.i±)-BflAT(NCi±)CMT(NN) 3390 C 349 C ENFORCE ZERO DISPLACEMENT BOUNDARY

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

HIV-1 protease-substrate coevolution in nelfinavir resistance.

PubMed

Kolli, Madhavi; Ozen, Ayşegül; Kurt-Yilmaz, Nese; Schiffer, Celia A

2014-07-01

Resistance to various human immunodeficiency virus type 1 (HIV-1) protease inhibitors (PIs) challenges the effectiveness of therapies in treating HIV-1-infected individuals and AIDS patients. The virus accumulates mutations within the protease (PR) that render the PIs less potent. Occasionally, Gag sequences also coevolve with mutations at PR cleavage sites contributing to drug resistance. In this study, we investigated the structural basis of coevolution of the p1-p6 cleavage site with the nelfinavir (NFV) resistance D30N/N88D protease mutations by determining crystal structures of wild-type and NFV-resistant HIV-1 protease in complex with p1-p6 substrate peptide variants with L449F and/or S451N. Alterations of residue 30's interaction with the substrate are compensated by the coevolving L449F and S451N cleavage site mutations. This interdependency in the PR-p1-p6 interactions enhances intermolecular contacts and reinforces the overall fit of the substrate within the substrate envelope, likely enabling coevolution to sustain substrate recognition and cleavage in the presence of PR resistance mutations. Resistance to human immunodeficiency virus type 1 (HIV-1) protease inhibitors challenges the effectiveness of therapies in treating HIV-1-infected individuals and AIDS patients. Mutations in HIV-1 protease selected under the pressure of protease inhibitors render the inhibitors less potent. Occasionally, Gag sequences also mutate and coevolve with protease, contributing to maintenance of viral fitness and to drug resistance. In this study, we investigated the structural basis of coevolution at the Gag p1-p6 cleavage site with the nelfinavir (NFV) resistance D30N/N88D protease mutations. Our structural analysis reveals the interdependency of protease-substrate interactions and how coevolution may restore substrate recognition and cleavage in the presence of protease drug resistance mutations. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Measurement of inclusive jet production and nuclear modifications in pPb collisions at √{s_{_NN}} =5.02 TeV

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Knünz, V.; König, A.; Krammer, M.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Heracleous, N.; Keaveney, J.; Lowette, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Van Parijs, I.; Barria, P.; Brun, H.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Fang, W.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Maerschalk, T.; Marinov, A.; Perniè, L.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Yonamine, R.; Zenoni, F.; Zhang, F.; Beernaert, K.; Benucci, L.; Cimmino, A.; Crucy, S.; Dobur, D.; Fagot, A.; Garcia, G.; Gul, M.; Mccartin, J.; Ocampo Rios, A. A.; Poyraz, D.; Ryckbosch, D.; Salva, S.; Sigamani, M.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; Ceard, L.; Delaere, C.; Delcourt, M.; Favart, D.; Forthomme, L.; Giammanco, A.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Mertens, A.; Musich, M.; Nuttens, C.; Perrini, L.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Beliy, N.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. 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F.; Missiroli, M.; Moran, D.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Palencia Cortezon, E.; Vizan Garcia, J. M.; Cabrillo, I. J.; Calderon, A.; Castiñeiras De Saa, J. R.; Curras, E.; De Castro Manzano, P.; Fernandez, M.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Bendavid, J.; Benhabib, L.; Berruti, G. M.; Bloch, P.; Bocci, A.; Bonato, A.; Botta, C.; Breuker, H.; Camporesi, T.; Castello, R.; Cepeda, M.; Cerminara, G.; D'Alfonso, M.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Guio, F.; De Roeck, A.; De Visscher, S.; Di Marco, E.; Dobson, M.; Dordevic, M.; Dorney, B.; du Pree, T.; Duggan, D.; Dünser, M.; Dupont, N.; Elliott-Peisert, A.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gill, K.; Giordano, D.; Girone, M.; Glege, F.; Guida, R.; Gundacker, S.; Guthoff, M.; Hammer, J.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kirschenmann, H.; Kortelainen, M. J.; Kousouris, K.; Krajczar, K.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Magini, N.; Malgeri, L.; Mannelli, M.; Martelli, A.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Nemallapudi, M. V.; Neugebauer, H.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Piparo, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Ruan, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Seidel, M.; Sharma, A.; Silva, P.; Simon, M.; Sphicas, P.; Steggemann, J.; Stieger, B.; Stoye, M.; Takahashi, Y.; Treille, D.; Triossi, A.; Tsirou, A.; Veres, G. I.; Wardle, N.; Wöhri, H. K.; Zagozdzinska, A.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Casal, B.; Dissertori, G.; Dittmar, M.; Donegà, M.; Eller, P.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Lecomte, P.; Lustermann, W.; Mangano, B.; Marionneau, M.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Rossini, M.; Schönenberger, M.; Starodumov, A.; Takahashi, M.; Tavolaro, V. R.; Theofilatos, K.; Wallny, R.; Aarrestad, T. K.; Amsler, C.; Caminada, L.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Galloni, C.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Ngadiuba, J.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Yang, Y.; Chen, K. H.; Doan, T. H.; Jain, Sh.; Khurana, R.; Konyushikhin, M.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Fiori, F.; Grundler, U.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Miñano Moya, M.; Petrakou, E.; Tsai, J. f.; Tzeng, Y. M.; Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Dumanoglu, I.; Girgis, S.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kayis Topaksu, A.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Sunar Cerci, D.; Tali, B.; Topakli, H.; Zorbilmez, C.; Bilin, B.; Bilmis, S.; Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Kaya, M.; Kaya, O.; Yetkin, E. A.; Yetkin, T.; Cakir, A.; Cankocak, K.; Sen, S.; Vardarlı, F. I.; Grynyov, B.; Levchuk, L.; Sorokin, P.; Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; Seif El Nasr-storey, S.; Senkin, S.; Smith, D.; Smith, V. J.; Belyaev, A.; Brew, C.; Brown, R. M.; Calligaris, L.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Worm, S. D.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Bundock, A.; Burton, D.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Dunne, P.; Elwood, A.; Futyan, D.; Hall, G.; Iles, G.; Lane, R.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Malik, S.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Seez, C.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leslie, D.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.; Alimena, J.; Benelli, G.; Berry, E.; Cutts, D.; Ferapontov, A.; Garabedian, A.; Hakala, J.; Heintz, U.; Jesus, O.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Syarif, R.; Breedon, R.; Breto, G.; De La Barca Sanchez, M. Calderon; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Cousins, R.; Everaerts, P.; Florent, A.; Hauser, J.; Ignatenko, M.; Saltzberg, D.; Takasugi, E.; Valuev, V.; Weber, M.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Ivova Paneva, M.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Malberti, M.; Negrete, M. Olmedo; Shrinivas, A.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D'Agnolo, R. T.; Derdzinski, M.; Holzner, A.; Kelley, R.; Klein, D.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Welke, C.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Flowers, K.; Franco Sevilla, M.; Geffert, P.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Incandela, J.; Mccoll, N.; Mullin, S. D.; Richman, J.; Stuart, D.; Suarez, I.; West, C.; Yoo, J.; Anderson, D.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhu, R. Y.; Andrews, M. B.; Azzolini, V.; Calamba, A.; Carlson, B.; Ferguson, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Ford, W. T.; Gaz, A.; Jensen, F.; Johnson, A.; Krohn, M.; Mulholland, T.; Nauenberg, U.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Chaves, J.; Chu, J.; Dittmer, S.; Eggert, N.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Sun, W.; Tan, S. M.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Wittich, P.; Abdullin, S.; Albrow, M.; Apollinari, G.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Hare, D.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Lewis, J.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Field, R. D.; Furic, I. K.; Konigsberg, J.; Korytov, A.; Kotov, K.; Ma, P.; Matchev, K.; Mei, H.; Milenovic, P.; Mitselmakher, G.; Rank, D.; Rossin, R.; Shchutska, L.; Snowball, M.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, J. R.; Adams, T.; Askew, A.; Bein, S.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Khatiwada, A.; Prosper, H.; Weinberg, M.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Kalakhety, H.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Kurt, P.; O'Brien, C.; Sandoval Gonzalez, l. D.; Turner, P.; Varelas, N.; Wu, Z.; Zakaria, M.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Anderson, I.; Barnett, B. A.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Osherson, M.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; Xin, Y.; You, C.; Baringer, P.; Bean, A.; Bruner, C.; Kenny, R. P.; Majumder, D.; Malek, M.; Mcbrayer, W.; Murray, M.; Sanders, S.; Stringer, R.; Wang, Q.; Ivanov, A.; Kaadze, K.; Khalil, S.; Makouski, M.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Lange, D.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Lu, Y.; Mignerey, A. C.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Baty, A.; Bi, R.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; Demiragli, Z.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Sumorok, K.; Tatar, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Benvenuti, A. C.; Dahmes, B.; Evans, A.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bartek, R.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Knowlton, D.; Kravchenko, I.; Meier, F.; Monroy, J.; Ratnikov, F.; Siado, J. E.; Snow, G. R.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Zhang, J.; Bhattacharya, S.; Hahn, K. A.; Kubik, A.; Low, J. F.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Ling, T. Y.; Liu, B.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Zuranski, A.; Malik, S.; Barker, A.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Jung, K.; Kumar, A.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Chou, J. P.; Contreras-Campana, E.; Ferencek, D.; Gershtein, Y.; Halkiadakis, E.; Heindl, M.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Lath, A.; Nash, K.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Foerster, M.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Krutelyov, V.; Mueller, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Rose, A.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Undleeb, S.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Wood, J.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Sarangi, T.; Savin, A.; Sharma, A.; Smith, N.; Smith, W. H.; Taylor, D.; Verwilligen, P.; Woods, N.; CMS Collaboration

2016-07-01

Inclusive jet production in pPb collisions at a nucleon-nucleon (NN) center-of-mass energy of √{s_{_NN}} =5.02 TeV is studied with the CMS detector at the LHC. A data sample corresponding to an integrated luminosity of 30.1 nb^{-1} is analyzed. The jet transverse momentum spectra are studied in seven pseudorapidity intervals covering the range -2.0<η _{CM}< 1.5 in the NN center-of-mass frame. The jet production yields at forward and backward pseudorapidity are compared and no significant asymmetry about η _{CM} = 0 is observed in the measured kinematic range. The measurements in the pPb system are compared to reference jet spectra obtained by extrapolation from previous measurements in pp collisions at √{s}=7 TeV . In all pseudorapidity ranges, nuclear modifications in inclusive jet production are found to be small, as predicted by next-to-leading order perturbative QCD calculations that incorporate nuclear effects in the parton distribution functions.

Azolla-Anabaena Relationship 1

PubMed Central

Meeks, John C.; Steinberg, Nisan A.; Enderlin, Carol S.; Joseph, Cecillia M.; Peters, Gerald A.

1987-01-01

The major radioactive products of the fixation of [13N]N2 by Azolla caroliniana Willd.-Anabaena azollae Stras. were ammonium, glutamine, and glutamate, plus a small amount of alanine. Ammonium accounted for 70 and 32% of the total radioactivity recovered after fixation for 1 and 10 minutes, respectively. The presence of a substantial pool of [13N]N2-derived 13NH4+ after longer incubation periods was attributed to the spatial separation between the site of N2-fixation (Anabaena) and a second, major site of assimilation (Azolla). Initially, glutamine was the most highly radioactive organic product formed from [13N]N2, but after 10 minutes of fixation glutamate had 1.5 times more radiolabel than glutamine. These kinetics of radiolabeling, along with the effects of inhibitors of glutamine synthetase and glutamate synthase on assimilation of exogenous and [13N]N2-derived 13NH4+, indicate that ammonium assimilation occurred by the glutamate synthase cycle and that glutamate dehydrogenase played little or no role in the synthesis of glutamate by Azolla-Anabaena. PMID:16665538

Quantum Monte Carlo calculations of neutron matter with chiral three-body forces

DOE PAGES

Tews, I.; Gandolfi, Stefano; Gezerlis, A.; ...

2016-02-02

Chiral effective field theory (EFT) enables a systematic description of low-energy hadronic interactions with controlled theoretical uncertainties. For strongly interacting systems, quantum Monte Carlo (QMC) methods provide some of the most accurate solutions, but they require as input local potentials. We have recently constructed local chiral nucleon-nucleon (NN) interactions up to next-to-next-to-leading order (N 2LO). Chiral EFT naturally predicts consistent many-body forces. In this paper, we consider the leading chiral three-nucleon (3N) interactions in local form. These are included in auxiliary field diffusion Monte Carlo (AFDMC) simulations. We present results for the equation of state of neutron matter and formore » the energies and radii of neutron drops. Specifically, we study the regulator dependence at the Hartree-Fock level and in AFDMC and find that present local regulators lead to less repulsion from 3N forces compared to the usual nonlocal regulators.« less

Forward Tracking with the JLab/MEIC Detector Concept

NASA Astrophysics Data System (ADS)

Hyde, Charles; JLab/MEIC Design Team

2015-10-01

At a future electron ion collider (EIC), the quark-gluon structure of the NN force can be probed in e . g . deeply virtual exclusive scattering on a tensor polarized Deuteron and diffractive DIS on the deuteron with tagging of the NN final state. The MEIC design includes two Interaction Points (IPs), each of which can operate simultaneously at full luminosity. The detector and beam-line optics for IP1 are designed to be nearly hermetic for all particles outside the presumed 10-sigma admittance (longitudinal and transverse) of the figure-8 accelerator lattice. The integration of the IP1 detector with the lattice extends 40 m downstream of the IP in both the electron and ion directions. The central region of the detector is a new 4 m long 3 m diameter 3 Tesla solenoid. Analysis in the forward ion direction is enhanced by the 50 mrad crossing angle at the IP, and a two-stage spectrometer integrated into the first 36 m of the accelerator lattice. In this talk I will present the optics and resolution of the forward ion spectrometer, including resolution effects of an initial beam pipe design. Supported by U.S. Department of Energy.

Army Training Study: Training Effectiveness Analysis (TEA) Summary. Volume 1. Armor.

DTIC Science & Technology

1978-08-08

c. r tf(e rx =r) rfs it ,, 0’( r’( r , a S’ f "’ u nn Ir Irf I I. * 222 ARM’- NN :TuDY TWANINC EFFCIUENEZ-: ANALISIS TEA : MMARY ’JLUM~ I ARMOR U...marginally above 50%, however, probably is not. 22 TABLE 10 TANK CREW QUALIFICATION PERFORMANCE ON TASK STANDARDS S TANDARD SATI S FACTORY Day

Remote sensing of nutrient deficiency in Lactuca sativa using neural networks for terrestrial and advanced life support applications

NASA Astrophysics Data System (ADS)

Sears, Edie Seldon

2000-12-01

A remote sensing study using reflectance and fluorescence spectra of hydroponically grown Lactuca sativa (lettuce) canopies was conducted. An optical receiver was designed and constructed to interface with a commercial fiber optic spectrometer for data acquisition. Optical parameters were varied to determine effects of field of view and distance to target on vegetation stress assessment over the test plant growth cycle. Feedforward backpropagation neural networks (NN) were implemented to predict the presence of canopy stress. Effects of spatial and spectral resolutions on stress predictions of the neural network were also examined. Visual inspection and fresh mass values failed to differentiate among controls, plants cultivated with 25% of the recommended concentration of phosphorous (P), and those cultivated with 25% nitrogen (N) based on fresh mass and visual inspection. The NN's were trained on input vectors created using reflectance and test day, fluorescence and test day, and reflectance, fluorescence, and test day. Four networks were created representing four levels of spectral resolution: 100-nm NN, 10-nm NN, 1-nm NN, and 0.1-nm NN. The 10-nm resolution was found to be sufficient for classifying extreme nitrogen deficiency in freestanding hydroponic lettuce. As a result of leaf angle and canopy structure broadband scattering intensity in the 700-nm to 1000-nm range was found to be the most useful portion of the spectrum in this study. More subtle effects of "greenness" and fluorescence emission were believed to be obscured by canopy structure and leaf orientation. As field of view was not as found to be as significant as originally believed, systems implementing higher repetitions over more uniformly oriented, i.e. smaller, flatter, target areas would provide for more discernible neural network input vectors. It is believed that this technique holds considerable promise for early detection of extreme nitrogen deficiency. Further research is recommended using stereoscopic digital cameras to quantify leaf area index, leaf shape, and leaf orientation as well as reflectance. Given this additional information fluorescence emission may also prove a more useful biological assay of freestanding vegetation.

Differential cross section for the 16O( t, p)18O reaction and determination of the size of the two-neutron periphery in the 18O nucleus

NASA Astrophysics Data System (ADS)

Galanina, L. I.; Zelenskaya, N. S.

2017-09-01

Within the theoretical formalism that combines a four-body problem with themultiparticle shell model, it is shown that the cross section for the dineuteron-stripping mechanism is consistent with the experimental angular distribution of protons from the 16O( t, p)18O reaction. This makes it possible to find the wave function for the relative motion of the dineutron and 16O and to obtain thereby the probability density W( r) for the dineutron in 18O, the nn-16O interaction potential, and the root-mean-square distance 〈 L〉 nn between the dineutron and 16O. The respective calculations reveal that, at r ≈ 8 fm, the dineutron probability density and a rather deep nn-16O potential become negligible, which leads to the absence of a dineuntron periphery in 18O. It seems that one can explain this fact by a rather large value (12.19 MeV) of the dineutron binding energy in this nucleus. Thus, the 18O nucleus is quite compact an object, despite the excess of two neutrons, and has a neutron skin rather than a periphery.

Interactive learning in 2×2 normal form games by neural network agents

NASA Astrophysics Data System (ADS)

Spiliopoulos, Leonidas

2012-11-01

This paper models the learning process of populations of randomly rematched tabula rasa neural network (NN) agents playing randomly generated 2×2 normal form games of all strategic classes. This approach has greater external validity than the existing models in the literature, each of which is usually applicable to narrow subsets of classes of games (often a single game) and/or to fixed matching protocols. The learning prowess of NNs with hidden layers was impressive as they learned to play unique pure strategy equilibria with near certainty, adhered to principles of dominance and iterated dominance, and exhibited a preference for risk-dominant equilibria. In contrast, perceptron NNs were found to perform significantly worse than hidden layer NN agents and human subjects in experimental studies.

Mechanistic insight into the hydrazine decomposition on Rh(111): effect of reaction intermediate on catalytic activity.

PubMed

Deng, Zhigang; Lu, Xiaoqing; Wen, Zengqiang; Wei, Shuxian; Liu, Yunjie; Fu, Dianling; Zhao, Lianming; Guo, Wenyue

2013-10-14

Periodic density functional theory (DFT) calculations have been performed to systematically investigate the effect of reaction intermediate on catalytic activity for hydrazine (N2H4) decomposition on Rh(111). Reaction mechanisms via intramolecular and NH2-assisted N2H4 decompositions are comparatively analyzed, including adsorption configuration, reaction energy and barrier of elementary step, and reaction network. Our results show that the most favorable N2H4 decomposition pathway starts with the initial N-N bond scission to the NH2 intermediate, followed by stepwise H stripping from adsorbed N2Hx (x = 1-4) species, and finally forms the N2 and NH3 products. Comparatively, the stepwise intramolecular dehydrogenation via N2H4→ N2H3→ N2H2→ N2H → N2, and N2H4→ NH2→ NH → N with or without NH2 promotion effect, are unfavorable due to higher energy barriers encountered. Energy barrier analysis, reaction rate constants, and electronic structures are used to identify the crucial competitive route. The promotion effect of the NH2 intermediate is structurally reflected in the weakening of the N-H bond and strengthening of the N-N bond in N2Hx in the coadsorption system; it results intrinsically from the less structural deformation of the adsorbate, and weakening of the interaction between dehydrogenated fragment and departing H in transition state. Our results highlight the crucial effect of reaction intermediate on catalytic activity and provide a theoretical approach to analyze the effect.

40 CFR Table Nn-2 to Subpart Hh of... - Lookup Default Values for Calculation Methodology 2 of This Subpart

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) MANDATORY GREENHOUSE GAS REPORTING Municipal Solid Waste Landfills Pt. 98, Subpt. NN, Table NN-2 Table NN-2 to Subpart HH of Part 98—Lookup Default Values...

Auto-Detection of Partial Discharges in Power Cables by Descrete Wavelet Transform

NASA Astrophysics Data System (ADS)

Yasuda, Yoh; Hara, Takehisa; Urano, Koji; Chen, Min

One of the serious problems that may happen in power XLPE cables is destruction of insulator. The best and conventional way to prevent such a crucial accident is generally supposed to ascertain partial corona discharges occurring at small void in organic insulator. However, there are some difficulties to detect those partial discharges because of existence of external noises in detected data, whose patterns are hardly identified at a glance. By the reason of the problem, there have been a number of researches on the way of development to accomplish detecting partial discharges by employing neural network (NN) system, which is widely known as the system for pattern recognition. We have been developing the NN system of the auto-detection for partial discharges, which we actually input numerical data of waveform itself into and obtained appropriate performance from. In this paper, we employed Descrete Wavelet Transform (DWT) to acquire more detailed transformed data in order to put them into the NN system. Employing DWT, we became able to express the waveform data in time-frequency space, and achieved effective detectiton of partial discharges by NN system. We present here the results using DWT analysis for partial discharges and noise signals which we obtained actually. Moreover, we present results out of the NN system which were dealt with those transformed data.

Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

NASA Astrophysics Data System (ADS)

Kariminia, Shahab; Motamedi, Shervin; Shamshirband, Shahaboddin; Piri, Jamshid; Mohammadi, Kasra; Hashim, Roslan; Roy, Chandrabhushan; Petković, Dalibor; Bonakdari, Hossein

2016-05-01

Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user's behavioural, physiological and psychological aspects. These aspects play critical roles in user's ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature ( T mrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the T mrt.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints.

PubMed

He, Pingan; Jagannathan, S

2007-04-01

A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the "strategic" utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysis.

Interaction of cationic carbosilane dendrimers and their complexes with siRNA with erythrocytes and red blood cell ghosts.

PubMed

Wrobel, Dominika; Kolanowska, Katarzyna; Gajek, Arkadiusz; Gomez-Ramirez, Rafael; de la Mata, Javier; Pedziwiatr-Werbicka, Elżbieta; Klajnert, Barbara; Waczulikova, Iveta; Bryszewska, Maria

2014-03-01

We have investigated the interactions between cationic NN16 and BDBR0011 carbosilane dendrimers with red blood cells or their cell membranes. The carbosilane dendrimers used possess 16 cationic functional groups. Both the dendrimers are made of water-stable carbon-silicon bonds, but NN16 possesses some oxygen-silicon bonds that are unstable in water. The nucleic acid used in the experiments was targeted against GAG-1 gene from the human immunodeficiency virus, HIV-1. By binding to the outer leaflet of the membrane, carbosilane dendrimers decreased the fluidity of the hydrophilic part of the membrane but increased the fluidity of the hydrophobic interior. They induced hemolysis, but did not change the morphology of the cells. Increasing concentrations of dendrimers induced erythrocyte aggregation. Binding of short interfering ribonucleic acid (siRNA) to a dendrimer molecule decreased the availability of cationic groups and diminished their cytotoxicity. siRNA-dendrimer complexes changed neither the fluidity of biological membranes nor caused cell hemolysis. Addition of dendriplexes to red blood cell suspension induced echinocyte formation. Copyright © 2013 Elsevier B.V. All rights reserved.

Methane dissociation on Ni(111): A fifteen-dimensional potential energy surface using neural network method

NASA Astrophysics Data System (ADS)

Shen, Xiangjian; Chen, Jun; Zhang, Zhaojun; Shao, Kejie; Zhang, Dong H.

2015-10-01

In the present work, we develop a highly accurate, fifteen-dimensional potential energy surface (PES) of CH4 interacting on a rigid flat Ni(111) surface with the methodology of neural network (NN) fit to a database consisted of about 194 208 ab initio density functional theory (DFT) energy points. Some careful tests of the accuracy of the fitting PES are given through the descriptions of the fitting quality, vibrational spectrum of CH4 in vacuum, transition state (TS) geometries as well as the activation barriers. Using a 25-60-60-1 NN structure, we obtain one of the best PESs with the least root mean square errors: 10.11 meV for the entrance region and 17.00 meV for the interaction and product regions. Our PES can reproduce the DFT results very well in particular for the important TS structures. Furthermore, we present the sticking probability S0 of ground state CH4 at the experimental surface temperature using some sudden approximations by Jackson's group. An in-depth explanation is given for the underestimated sticking probability.

Surface wind mixing in the Regional Ocean Modeling System (ROMS)

NASA Astrophysics Data System (ADS)

Robertson, Robin; Hartlipp, Paul

2017-12-01

Mixing at the ocean surface is key for atmosphere-ocean interactions and the distribution of heat, energy, and gases in the upper ocean. Winds are the primary force for surface mixing. To properly simulate upper ocean dynamics and the flux of these quantities within the upper ocean, models must reproduce mixing in the upper ocean. To evaluate the performance of the Regional Ocean Modeling System (ROMS) in replicating the surface mixing, the results of four different vertical mixing parameterizations were compared against observations, using the surface mixed layer depth, the temperature fields, and observed diffusivities for comparisons. The vertical mixing parameterizations investigated were Mellor- Yamada 2.5 level turbulent closure (MY), Large- McWilliams- Doney Kpp (LMD), Nakanishi- Niino (NN), and the generic length scale (GLS) schemes. This was done for one temperate site in deep water in the Eastern Pacific and three shallow water sites in the Baltic Sea. The model reproduced the surface mixed layer depth reasonably well for all sites; however, the temperature fields were reproduced well for the deep site, but not for the shallow Baltic Sea sites. In the Baltic Sea, the models overmixed the water column after a few days. Vertical temperature diffusivities were higher than those observed and did not show the temporal fluctuations present in the observations. The best performance was by NN and MY; however, MY became unstable in two of the shallow simulations with high winds. The performance of GLS nearly as good as NN and MY. LMD had the poorest performance as it generated temperature diffusivities that were too high and induced too much mixing. Further observational comparisons are needed to evaluate the effects of different stratification and wind conditions and the limitations on the vertical mixing parameterizations.

Bacterial adhesion force quantification by fluidic force microscopy

NASA Astrophysics Data System (ADS)

Potthoff, Eva; Ossola, Dario; Zambelli, Tomaso; Vorholt, Julia A.

2015-02-01

Quantification of detachment forces between bacteria and substrates facilitates the understanding of the bacterial adhesion process that affects cell physiology and survival. Here, we present a method that allows for serial, single bacterial cell force spectroscopy by combining the force control of atomic force microscopy with microfluidics. Reversible bacterial cell immobilization under physiological conditions on the pyramidal tip of a microchanneled cantilever is achieved by underpressure. Using the fluidic force microscopy technology (FluidFM), we achieve immobilization forces greater than those of state-of-the-art cell-cantilever binding as demonstrated by the detachment of Escherichia coli from polydopamine with recorded forces between 4 and 8 nN for many cells. The contact time and setpoint dependence of the adhesion forces of E. coli and Streptococcus pyogenes, as well as the sequential detachment of bacteria out of a chain, are shown, revealing distinct force patterns in the detachment curves. This study demonstrates the potential of the FluidFM technology for quantitative bacterial adhesion measurements of cell-substrate and cell-cell interactions that are relevant in biofilms and infection biology.Quantification of detachment forces between bacteria and substrates facilitates the understanding of the bacterial adhesion process that affects cell physiology and survival. Here, we present a method that allows for serial, single bacterial cell force spectroscopy by combining the force control of atomic force microscopy with microfluidics. Reversible bacterial cell immobilization under physiological conditions on the pyramidal tip of a microchanneled cantilever is achieved by underpressure. Using the fluidic force microscopy technology (FluidFM), we achieve immobilization forces greater than those of state-of-the-art cell-cantilever binding as demonstrated by the detachment of Escherichia coli from polydopamine with recorded forces between 4 and 8 nN for many cells. The contact time and setpoint dependence of the adhesion forces of E. coli and Streptococcus pyogenes, as well as the sequential detachment of bacteria out of a chain, are shown, revealing distinct force patterns in the detachment curves. This study demonstrates the potential of the FluidFM technology for quantitative bacterial adhesion measurements of cell-substrate and cell-cell interactions that are relevant in biofilms and infection biology. Electronic supplementary information (ESI) available: Video S1. Detachment of a S. pyogenes cell chain from glass substrate. The cantilever is approached on the outermost adherent cell of a chain and four bacteria were then sequentially detached. The sequential cell detachment suddenly stopped after four bacteria. This possibly occurred because bacteria-glass interactions became too strong or the maximal probe retraction was reached. The cells spontaneously detached from the cantilever flipping back on the surface. Fig. S1. (A) Adhesion force-distance and (B) adhesion force-detaching work correlation of E.coli on PLL for setpoints of 1 and 10 nN. Circle: 1 nN setpoint, square: 10 nN. See DOI: 10.1039/c4nr06495j

Inverse analysis of turbidites by machine learning

NASA Astrophysics Data System (ADS)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small deviation from the true parameters. Comparing to previous inverse modeling of turbidity currents, our methodology is superior especially in the efficiency of computation. Also, our methodology has advantage in extensibility and applicability to various sediment transport processes such as pyroclastic flows or debris flows.

Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems.

PubMed

Dai, Shi-Lu; Wang, Cong; Wang, Min

2014-01-01

This paper studies the problem of learning from adaptive neural network (NN) control of a class of nonaffine nonlinear systems in uncertain dynamic environments. In the control design process, a stable adaptive NN tracking control design technique is proposed for the nonaffine nonlinear systems with a mild assumption by combining a filtered tracking error with the implicit function theorem, input-to-state stability, and the small-gain theorem. The proposed stable control design technique not only overcomes the difficulty in controlling nonaffine nonlinear systems but also relaxes constraint conditions of the considered systems. In the learning process, the partial persistent excitation (PE) condition of radial basis function NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition and an appropriate state transformation, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the implicit desired control input dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, an NN learning control design technique that effectively exploits the learned knowledge without re-adapting to the controller parameters is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.

Design of fuzzy system by NNs and realization of adaptability

NASA Technical Reports Server (NTRS)

Takagi, Hideyuki

1993-01-01

The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

Cost-Effectiveness of Management Activities Related to the Interaction between the California Sea Lion and the Southern California Shark Gill-Net Fishery.

DTIC Science & Technology

1983-03-01

t:ar czly he mad=- after all ths eccnoa_-c a*:a social -- cot cn,c1:..nc State manaqpm=rnt hav-= cezn studie;cd. Such a analysis fc= all. th-, fishe...rsula- the pc-ul-- nn aze not known. To cope with these problems An procedure, :elferred :o as the= lyzamlc response me:thcd (DM) was %dev=eloped and is_...19E2 d(:llars amour :-s 7o $726. These reu..ssuccest : -:he tc-al estimaiand loss in 1932 was prxatlJ 16. 4. Valua cf Acous-,:al ?lavbac k -i:t

Constraining the magnitude of the Chiral Magnetic Effect with Event Shape Engineering in Pb–Pb collisions at s NN = 2.76  TeV

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

Acharya, S.; Adam, J.; Adamová, D.

In ultrarelativistic heavy-ion collisions, the event-by-event variation of the elliptic flow v 2 reflects fluctuations in the shape of the initial state of the system. This allows to select events with the same centrality but different initial geometry. This selection technique, Event Shape Engineering, has been used in the analysis of charge-dependent two- and three-particle correlations in Pb–Pb collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=2.76 TeV. The two-particle correlator 〈cos⁡(φ α -φ β)〉 calculated for different combinations of charges α and β is almost independent of v 2 (for a given centrality), while the three-particle correlator 〈cos⁡(φ α +φ β -2Ψ 2)〉 scales almost linearly both with the event v 2 and charged-particle pseudorapidity density. The charge dependence of the three-particle correlator is often interpreted as evidence for the Chiral Magnetic Effect (CME), a parity violating effect of the strong interaction. However, its measured dependence on v 2 points to a large non-CME contribution to the correlator. Finally, comparing the results with Monte Carlo calculations including a magnetic field due to the spectators, the upper limit of the CME signal contribution to the three-particle correlator in the 10–50% centrality interval is found to be 26–33% at 95% confidence level.« less

Constraining the magnitude of the Chiral Magnetic Effect with Event Shape Engineering in Pb–Pb collisions at s NN = 2.76  TeV

DOE PAGES

Acharya, S.; Adam, J.; Adamová, D.; ...

2017-12-12

In ultrarelativistic heavy-ion collisions, the event-by-event variation of the elliptic flow v 2 reflects fluctuations in the shape of the initial state of the system. This allows to select events with the same centrality but different initial geometry. This selection technique, Event Shape Engineering, has been used in the analysis of charge-dependent two- and three-particle correlations in Pb–Pb collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=2.76 TeV. The two-particle correlator 〈cos⁡(φ α -φ β)〉 calculated for different combinations of charges α and β is almost independent of v 2 (for a given centrality), while the three-particle correlator 〈cos⁡(φ α +φ β -2Ψ 2)〉 scales almost linearly both with the event v 2 and charged-particle pseudorapidity density. The charge dependence of the three-particle correlator is often interpreted as evidence for the Chiral Magnetic Effect (CME), a parity violating effect of the strong interaction. However, its measured dependence on v 2 points to a large non-CME contribution to the correlator. Finally, comparing the results with Monte Carlo calculations including a magnetic field due to the spectators, the upper limit of the CME signal contribution to the three-particle correlator in the 10–50% centrality interval is found to be 26–33% at 95% confidence level.« less

Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

NASA Astrophysics Data System (ADS)

Manikumari, N.; Murugappan, A.; Vinodhini, G.

2017-07-01

Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

PubMed

Akl, A I; Sobh, M A; Enab, Y M; Tattersall, J

2001-12-01

The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Evidence that PC2 is the endogenous pro-neurotensin convertase in rMTC 6-23 cells and that PC1- and PC2-transfected PC12 cells differentially process pro-neurotensin.

PubMed

Rovère, C; Barbero, P; Kitabgi, P

1996-05-10

The neuropeptide precursor proneurotensin/neuromedin N (pro-NT/NN) is mainly expressed and differentially processed in the brain and in the small intestine. We showed previously that rMTC 6-23 cells process pro-NT/NN with a pattern similar to brain tissue and increase pro-NT/NN expression in response to dexamethasone, and that PC12 cells also produce pro-NT/NN but are virtually unable to process it. In addition, PC12 cells were reported to be devoid of the prohormone convertases PC1 and PC2. The present study was designed to identify the proprotein convertase(s) (PC) involved in pro-NT/NN processing in rMTC 6-23 cells and to compare PC1- and PC2-transfected PC12 cells for their ability to process pro-NT/NN. rMTC 6-23 cells were devoid of PC1, PC4, and PC5 but expressed furin and PC2. Stable expression of antisense PC2 RNA in rMTC 6-23 cells led to a 90% decrease in PC2 protein levels that correlated with a > 80% reduction of pro-NT/NN processing. PC2 expression was stimulated by dexamethasone in a time- and concentration-dependent manner. Stable PC12/PC2 transfectants processed pro-NT/NN with a pattern similar to that observed in the brain and in rMTC 6-23 cells. In contrast, stable PC12/PC1 transfectants reproduced the pro-NT/NN processing pattern seen in the gut. We conclude that (i) PC2 is the major pro-NT/NN convertase in rMTC 6-23 cells; (ii) its expression is coregulated with that of pro-NT/NN in this cell line; and (iii) PC2 and PC1 differentially process pro-NT/NN with brain and intestinal phenotype, respectively.

Generalized seniority on a deformed single-particle basis

NASA Astrophysics Data System (ADS)

Jia, L. Y.

2017-09-01

Recently, I proposed a fast computing scheme for generalized seniority on a spherical single-particle basis [J. Phys. G: Nucl. Part. Phys. 42, 115105 (2015), 10.1088/0954-3899/42/11/115105]. This work redesigns the scheme to make it applicable to deformed single-particle basis. The algorithm is applied to the rare-earth-metal nucleus 94 64 158Gd for intrinsic (body-fixed frame) neutron excitations under the low-momentum NN interaction Vlow -k. By allowing as many as four broken pairs, I compute the lowest 300 intrinsic states of several multipolarities. These states converge well to the exact ones, showing generalized seniority is very effective in truncating the deformed shell model. Under realistic interactions, the picture remains approximately valid: The ground state is a coherent pair condensate and the pairs gradually break up as excitation energy increases.

The effect of normal load on polytetrafluoroethylene tribology

NASA Astrophysics Data System (ADS)

Barry, Peter R.; Chiu, Patrick Y.; Perry, Scott S.; Sawyer, W. Gregory; Phillpot, Simon R.; Sinnott, Susan B.

2009-04-01

The tribological behavior of oriented poly(tetrafluoroethylene) (PTFE) sliding surfaces is examined as a function of sliding direction and applied normal load in classical molecular dynamics (MD) simulations. The forces are calculated with the second-generation reactive empirical bond-order potential for short-range interactions, and with a Lennard-Jones potential for long-range interactions. The range of applied normal loads considered is 5-30 nN. The displacement of interfacial atoms from their initial positions during sliding is found to vary by a factor of seven, depending on the relative orientation of the sliding chains. However, within each sliding configuration the magnitude of the interfacial atomic displacements exhibits little dependence on load over the range considered. The predicted friction coefficients are also found to vary with chain orientation and are in excellent quantitative agreement with experimental measurements.

Nearest neighbor-density-based clustering methods for large hyperspectral images

NASA Astrophysics Data System (ADS)

Cariou, Claude; Chehdi, Kacem

2017-10-01

We address the problem of hyperspectral image (HSI) pixel partitioning using nearest neighbor - density-based (NN-DB) clustering methods. NN-DB methods are able to cluster objects without specifying the number of clusters to be found. Within the NN-DB approach, we focus on deterministic methods, e.g. ModeSeek, knnClust, and GWENN (standing for Graph WatershEd using Nearest Neighbors). These methods only require the availability of a k-nearest neighbor (kNN) graph based on a given distance metric. Recently, a new DB clustering method, called Density Peak Clustering (DPC), has received much attention, and kNN versions of it have quickly followed and showed their efficiency. However, NN-DB methods still suffer from the difficulty of obtaining the kNN graph due to the quadratic complexity with respect to the number of pixels. This is why GWENN was embedded into a multiresolution (MR) scheme to bypass the computation of the full kNN graph over the image pixels. In this communication, we propose to extent the MR-GWENN scheme on three aspects. Firstly, similarly to knnClust, the original labeling rule of GWENN is modified to account for local density values, in addition to the labels of previously processed objects. Secondly, we set up a modified NN search procedure within the MR scheme, in order to stabilize of the number of clusters found from the coarsest to the finest spatial resolution. Finally, we show that these extensions can be easily adapted to the three other NN-DB methods (ModeSeek, knnClust, knnDPC) for pixel clustering in large HSIs. Experiments are conducted to compare the four NN-DB methods for pixel clustering in HSIs. We show that NN-DB methods can outperform a classical clustering method such as fuzzy c-means (FCM), in terms of classification accuracy, relevance of found clusters, and clustering speed. Finally, we demonstrate the feasibility and evaluate the performances of NN-DB methods on a very large image acquired by our AISA Eagle hyperspectral imaging sensor.

Immunological and biochemical characterization of processing products from the neurotensin/neuromedin N precursor in the rat medullary thyroid carcinoma 6-23 cell line.

PubMed Central

Bidard, J N; de Nadai, F; Rovere, C; Moinier, D; Laur, J; Martinez, J; Cuber, J C; Kitabgi, P

1993-01-01

Neurotensin (NT) and neuromedin N (NN) are two related biologically active peptides that are encoded in the same precursor molecule. In the rat, the precursor consists of a 169-residue polypeptide starting with an N-terminal signal peptide and containing in its C-terminal region one copy each of NT and NN. NN precedes NT and is separated from it by a Lys-Arg sequence. Two other Lys-Arg sequences flank the N-terminus of NN and the C-terminus of NT. A fourth Lys-Arg sequence occurs near the middle of the precursor and is followed by an NN-like sequence. Finally, an Arg-Arg pair is present within the NT moiety. The four Lys-Arg doublets represent putative processing sites in the precursor molecule. The present study was designed to investigate the post-translational processing of the NT/NN precursor in the rat medullary thyroid carcinoma (rMTC) 6-23 cell line, which synthesizes large amounts of NT upon dexamethasone treatment. Five region-specific antisera recognizing the free N- or C-termini of sequences adjacent to the basic doublets were produced, characterized and used for immunoblotting and radioimmunoassay studies in combination with gel filtration, reverse-phase h.p.l.c. and trypsin digestion of rMTC 6-23 cell extracts. Because two of the antigenic sequences, i.e. NN and the NN-like sequence, start with a lysine residue that is essential for recognition by their respective antisera, a micromethod by which trypsin specifically cleaves at arginine residues was developed. The results show that dexamethasone-treated rMTC 6-23 cells produced comparable amounts of NT, NN and a peptide corresponding to a large N-terminal precursor fragment lacking the NN and NT moieties. This large fragment was purified. N-Terminal sequencing revealed that it started at residue Ser23 of the prepro-NT/NN sequence, and thus established the Cys22-Ser23 bond as the cleavage site of the signal peptide. Two other large N-terminal fragments bearing respectively the NN and NT sequences at their C-termini were present in lower amounts. The NN-like sequence was internal to all the large fragments. There was no evidence for the presence of peptides with the NN-like sequence at their N-termini. This shows that, in rMTC 6-23 cells, the precursor is readily processed at the three Lys-Arg doublets that flank and separate the NT and NN sequences. In contrast, the Lys-Arg doublet that precedes the NN-like sequence is not processed in this system.(ABSTRACT TRUNCATED AT 400 WORDS) Images Figure 3 PMID:8471039

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

Akaishi, Yoshinori; College of Science and Technology, Nihon University, Funabashi 274-8501; Myint, Khin Swe

The overbinding problem of {sub {lambda}}{sup 5}He is solved by introducing a concept of coherent {lambda}-{sigma} coupling which is equivalent to a {lambda}NN three-body force. This three-body force is coherently enhanced in the 0{sup +} states of {sub {lambda}}{sup 4}H and {sub {lambda}}{sup 4}He. The 0{sup +}-1{sup +} splitting in these hypernuclei is mainly due to coherent {lambda}-{sigma} coupling and partly due to the {lambda}N spin-spin interaction. A {lambda}NN three-body potential is derived from the coupled-channel treatment. The origin of the repulsive and attractive nature of the three-body force is discussed. Coherent {lambda}-{sigma} coupling becomes more important in neutron-rich hypernucleimore » and especially in neutron-star matter at high densities. The possible existence of ''hyperheavy hydrogen'', {sub {lambda}}{sup 6}H, is suggested.« less

Identified particle v2 and v4 in Au+Au collisions at √s_NN =62, 130 and 200 GeV

NASA Astrophysics Data System (ADS)

Bai, Yuting

2004-10-01

The measured large elliptic flow v2 is interpreted as an indication of early local equilibrium[1,2] and is relevant to interpretations involving a strongly interacting quark-gluon plasma phase. v4 is argued to be more sensitive than v2 to initial conditions in hydrodynamic calculations[3]. We will present identified particle v2 and v4 measurements at √s_NN = 62, 130 and 200 GeV. The comparisons to hydro calculations will be shown, and the energy dependence of v2 as a function of transverse momentum will be addressed and discussed. [1] H.Sorge, Phys. Rev. Lett. 78, 2309 (1997). [2] P.F.Kolb and U.Heinz, nucl-th/0305084. [3] P.F.Kolb, Phys. Rev. C 68,031902(2003).

Collision geometry scaling of Au+Au pseudorapidity density from √(sNN )=19.6 to 200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steinberg, P.; Stephans, G. S.; Sukhanov, A.; Tonjes, M. B.; Tang, J.-L.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2004-08-01

The centrality dependence of the midrapidity charged particle multiplicity in Au+Au heavy-ion collisions at √(sNN )=19.6 and 200 GeV is presented. Within a simple model, the fraction of hard (scaling with number of binary collisions) to soft (scaling with number of participant pairs) interactions is consistent with a value of x=0.13±0.01 (stat) ±0.05 (syst) at both energies. The experimental results at both energies, scaled by inelastic p ( p¯ ) +p collision data, agree within systematic errors. The ratio of the data was found not to depend on centrality over the studied range and yields a simple linear scale factor of R200/19.6 =2.03±0.02 (stat) ±0.05 (syst) .

Systematics of nuclear deformation in large regions

NASA Astrophysics Data System (ADS)

Zhao, Y. M.; Arima, A.; Casten, R. F.

2001-06-01

In this paper we present the systematics of nuclear deformation for even-even, even-odd, odd-even, and doubly odd nuclei in four regions: the 50

Nearest Neighbor Classification of Stationary Time Series: An Application to Anesthesia Level Classification by EEG Analysis.

DTIC Science & Technology

1980-12-05

classification procedures that are common in speech processing. The anesthesia level classification by EEG time series population screening problem example is in...formance. The use of the KL number type metric in NN rule classification, in a delete-one subj ect ’s EE-at-a-time KL-NN and KL- kNN classification of the...17 individual labeled EEG sample population using KL-NN and KL- kNN rules. The results obtained are shown in Table 1. The entries in the table indicate

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

Accelerator diagnosis and control by Neural Nets

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

Spencer, J.E.

1989-01-01

Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach tomore » 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.« less

Measurement of inclusive jet production and nuclear modifications in pPb collisions at $$\\sqrt{s_{_\\mathrm {NN}}} =5.02\\,\\mathrm{TeV} $$

DOE PAGES

Khachatryan, Vardan

2016-07-04

In this study, inclusive jet production in pPb collisions at a nucleon–nucleon (NN) center-of-mass energy of √ sNN = 5.02 TeV is studied with the CMS detector at the LHC. A data sample corresponding to an integrated luminosity of 30.1 nb -1 is analyzed. The jet transverse momentum spectra are studied in seven pseudorapidity intervals covering the range -2.0 < η CM < 1.5 in the NN center-of-mass frame. The jet production yields at forward and backward pseudorapidity are compared and no significant asymmetry about ηCM=0 is observed in the measured kinematic range. The measurements in the pPb system aremore » compared to reference jet spectra obtained by extrapolation from previous measurements in pp collisions at √s = 7 TeV. In all pseudorapidity ranges, nuclear modifications in inclusive jet production are found to be small, as predicted by next-to-leading order perturbative QCD calculations that incorporate nuclear effects in the parton distribution functions.« less

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

PubMed Central

Vargas-Meléndez, Leandro; Boada, Beatriz L.; Boada, María Jesús L.; Gauchía, Antonio; Díaz, Vicente

2016-01-01

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator. PMID:27589763

Neural Network (NN) retrievals of Stratocumulus cloud properties using multi-angle polarimetric observations during ORACLES

NASA Astrophysics Data System (ADS)

Segal-Rosenhaimer, M.; Knobelspiesse, K. D.; Redemann, J.; Cairns, B.; Alexandrov, M. D.

2016-12-01

The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign is taking place in the South-East Atlantic during the Austral Spring for three consecutive years from 2016-2018. The study area encompasses one of the Earth's three semi-permanent subtropical Stratocumulus (Sc) cloud decks, and experiences very large aerosol optical depths, mainly biomass burning, originating from Africa. Over time, cloud optical depth (COD), lifetime and cloud microphysics (number concentration, effective radii Reff and precipitation) are expected to be influenced by indirect aerosol effects. These changes play a key role in the energetic balance of the region, and are part of the core investigation objectives of the ORACLES campaign, which acquires measurements of clean and polluted scenes of above cloud aerosols (ACA). Simultaneous retrievals of aerosol and cloud optical properties are being developed (e.g. MODIS, OMI), but still challenging, especially for passive, single viewing angle instruments. By comparison, multiangle polarimetric instruments like RSP (Research Scanning Polarimeter) show promise for detection and quantification of ACA, however, there are no operational retrieval algorithms available yet. Here we describe a new algorithm to retrieve cloud and aerosol optical properties from observations by RSP flown on the ER-2 and P-3 during the 2016 ORACLES campaign. The algorithm is based on training a NN, and is intended to retrieve aerosol and cloud properties simultaneously. However, the first step was to establish the retrieval scheme for low level Sc cloud optical properties. The NN training was based on simulated RSP total and polarized radiances for a range of COD, Reff, and effective variances, spanning 7 wavelength bands and 152 viewing zenith angles. Random and correlated noise were added to the simulations to achieve a more realistic representation of the signals. Before introducing the input variables to the network, the signals are projected on a principle component plane that retains the maximal signal information but minimizes the noise contribution. We will discuss parameter choices for the network and present preliminary results of cloud retrievals from ORACLES, compared with standard RSP low-level cloud retrieval method that has been validated against in situ observations.

Exploring the source of the mid-level hump for intensity discrimination in quiet and the effects of noisea)

PubMed Central

Roverud, Elin; Strickland, Elizabeth A.

2015-01-01

Intensity discrimination Weber fractions (WFs) measured for short, high-frequency tones in quiet are larger at mid levels than at lower or higher levels. The source of this “mid-level hump” is a matter of debate. One theory is that the mid-level hump reflects basilar-membrane compression, and that WFs decrease at higher levels due to spread-of-excitation cues. To test this theory, Experiment 1 measured the mid-level hump and growth-of-masking functions to estimate the basilar membrane input/output (I/O) function in the same listeners. Results showed the initial rise in WFs could be accounted for by the change in I/O function slope, but there was additional unexplained variability in WFs. Previously, Plack [(1998). J. Acoust. Soc. Am. 103(5), 2530–2538] showed that long-duration notched noise (NN) presented with the tone reduced the mid-level hump even with a temporal gap in the NN. Plack concluded the results were consistent with central profile analysis. However, simultaneous, forward, and backward NN were not examined separately, which may independently test peripheral and central mechanisms of the NN. Experiment 2 measured WFs at the mid-level hump in the presence of NN and narrowband noise of different durations and temporal positions relative to the tone. Results varied across subjects, but were consistent with more peripheral mechanisms. PMID:25786945

Listening to Mozart K.448 decreases electroencephalography oscillatory power associated with an increase in sympathetic tone in adults: a post-intervention study

PubMed Central

Lin, Lung-Chang; Ouyang, Chen-Sen; Chiang, Ching-Tai; Wu, Rong-Ching; Wu, Hui-Chuan

2014-01-01

Summary Objective Listening to Mozart K.448 has been demonstrated to improve spatial task scores, leading to what is known as the Mozart Effect. However, most of these reports only describe the phenomena but lack the scientific evidence needed to properly investigate the mechanism of Mozart Effect. In this study, we used electroencephalography (EEG) and heart rate variability (HRV) to evaluate the effects of Mozart K.448 on healthy volunteers to explore Mozart Effect. Design An EEG-based post-intervention analysis. Setting Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. Participants Twenty-nine college students were enrolled. They received EEG and electrocardiogram examinations simultaneously before, during and after listening to the first movement of Mozart K.448. Main outcome measure EEG alpha, theta and beta power and HRV were compared in each stage. Results The results showed a significant decrease in alpha, theta and beta power when they listened to Mozart K.448. In addition, the average root mean square successive difference, the proportion derived by dividing NN50 by the total number of NN intervals, standard deviations of NN intervals and standard deviations of differences between adjacent NN intervals showed a significant decrease, while the high frequency revealed a significant decrease with a significantly elevated low-frequency/high-frequency ratio. Conclusion Listening to Mozart K.448 significantly decreased EEG alpha, theta and beta power and HRV. This study indicates that there is brain cortical function and sympathetic tone activation in healthy adults when listening to Mozart K.448, which may play an important role in the mechanism of Mozart Effect. PMID:25383198

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

Atamanik, E.; Thangadurai, V.

We report a comparative study of the dielectric properties of solid-state (ceramic method) synthesized NaNbO{sub 3} (NN), Na{sub 0.75}K{sub 0.25}NbO{sub 3} (K25NN), K{sub 0.5}Na{sub 0.5}NbO{sub 3} (KNN) and some composite materials containing In{sub 2}O{sub 3} and NN or KNN using an AC impedance method. Powder X-ray diffraction (PXRD) was employed to investigate the phase purity. No significant amount of impurity phase was observed for NN, K25NN, and KNN. Substitutions of 10, 15 and 25 mol% In{sup 3+} for Nb{sup 5+} in KNN and NN using solid-state reactions at 1150 deg. C resulted in composite materials. AC impedance studies of NN,more » KNN and K25NN in the temperature range of 500-800 deg. C showed a single semicircle (attributed to the bulk property) in the high-frequency range of 10{sup 3} to 10{sup 6} Hz. The individual contributions from the bulk and grain boundary on the dielectric properties were resolved and quantified from the impedance data. The calculated dielectric values for NN were consistent with previously reported in the literature. 10% Indium based KNN composite materials had the lowest dielectric loss 0.585 and the dielectric constant of 233 at 100 kHz at the temperature of 650 deg. C.« less

Maternal Perinatal Diet Induces Developmental Programming of Bone Architecture

PubMed Central

Devlin, MJ; Grasemann, C; Cloutier, AM; Louis, L; Alm, C; Palmert, MR; Bouxsein, ML

2013-01-01

Maternal high fat diet can alter offspring metabolism via perinatal developmental programming. This study tests the hypothesis that maternal high fat diet also induces perinatal programming of offspring bone mass and strength. We compared skeletal acquisition in pups from C57Bl/6J mice fed high fat or normal diet from preconception through lactation. Three-week-old male and female pups from high fat (HF-N) and normal mothers (N-N) were weaned onto normal diet. Outcomes at 14 and 26 wks of age included body mass, body composition, whole body bone mineral content via pDXA, femoral cortical and trabecular architecture via μCT, and glucose tolerance. Female HF-N had normal body mass and glucose tolerance, with lower %body fat but higher serum leptin at 14 wks vs. N-N (p<0.05 for both). Whole body bone mineral content was 12% lower at 14 wks and 5% lower at 26 wks, but trabecular bone volume fraction was 20% higher at 14 wks in female HF-N vs. N-N (p<0.05 for all). Male HF-N had normal body mass and mildly impaired glucose tolerance, with lower %body fat at 14 wks and lower serum leptin at 26 wks vs. N-N (p<0.05 for both). Serum insulin was higher at 14 wks and lower at 26 wks in HF-N vs. N-N (p<0.05). Trabecular BV/TV was 34% higher and cortical bone area was 6% higher at 14 wks vs. N-N (p<0.05 for both). These data suggest maternal high fat diet has complex effects on offspring bone, supporting the hypothesis that maternal diet alters postnatal skeletal homeostasis. PMID:23503967

Maternal perinatal diet induces developmental programming of bone architecture.

PubMed

Devlin, M J; Grasemann, C; Cloutier, A M; Louis, L; Alm, C; Palmert, M R; Bouxsein, M L

2013-04-01

Maternal high-fat (HF) diet can alter offspring metabolism via perinatal developmental programming. This study tests the hypothesis that maternal HF diet also induces perinatal programming of offspring bone mass and strength. We compared skeletal acquisition in pups from C57Bl/6J mice fed HF or normal diet from preconception through lactation. Three-week-old male and female pups from HF (HF-N) and normal mothers (N-N) were weaned onto normal diet. Outcomes at 14 and 26 weeks of age included body mass, body composition, whole-body bone mineral content (WBBMC) via peripheral dual-energy X-ray absorptiometry, femoral cortical and trabecular architecture via microcomputed tomography, and glucose tolerance. Female HF-N had normal body mass and glucose tolerance, with lower body fat (%) but higher serum leptin at 14 weeks vs. N-N (P<0.05 for both). WBBMC was 12% lower at 14 weeks and 5% lower at 26 weeks, but trabecular bone volume fraction was 20% higher at 14 weeks in female HF-N vs. N-N (P<0.05 for all). Male HF-N had normal body mass and mildly impaired glucose tolerance, with lower body fat (%) at 14 weeks and lower serum leptin at 26 weeks vs. N-N (P<0.05 for both). Serum insulin was higher at 14 weeks and lower at 26 weeks in HF-N vs. N-N (P<0.05). Trabecular BV/TV was 34% higher and cortical bone area was 6% higher at 14 weeks vs. N-N (P<0.05 for both). These data suggest that maternal HF diet has complex effects on offspring bone, supporting the hypothesis that maternal diet alters postnatal skeletal homeostasis.

A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

PubMed Central

Zhao, Weixiang; Davis, Cristina E.

2011-01-01

Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. Conclusion In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems. PMID:21515033

Effect of Förster-mediated triplet-polaron quenching and triplet-triplet annihilation on the efficiency roll-off of organic light-emitting diodes

NASA Astrophysics Data System (ADS)

van Eersel, H.; Bobbert, P. A.; Janssen, R. A. J.; Coehoorn, R.

2016-04-01

We report the results of a systematic study of the interplay of triplet-polaron quenching (TPQ) and triplet-triplet annihilation (TTA) on the efficiency roll-off of organic light-emitting diodes (OLEDs) with increasing current density. First, we focus on OLEDs based on the green phosphorescent emitter tris[2-phenylpyridine]iridium(III) (Ir(ppy)3) and the red phosphorescent dye platinum octaethylporphyrin. It is found that the experimental data can be reproduced using kinetic Monte Carlo (kMC) simulations within which TPQ and TTA are due to a nearest-neighbor (NN) interaction, or due to a more long-range Förster-type process. Furthermore, we find a subtle interplay between TPQ and TTA: decreasing the contribution of one process can increase the contribution of the other process, so that the roll-off is not significantly reduced. Furthermore, we find that just analyzing the shape of the roll-off is insufficient for determining the relative role of TPQ and TTA. Subsequently, we investigate the wider validity of this picture using kMC simulations for idealized but realistic symmetric OLEDs, with an emissive layer containing a small concentration of phosphorescent dye molecules in a matrix material. Whereas for NN-interactions the roll-off can be reduced when the dye molecules act as shallow hole and electron traps, we find that such an approach becomes counterproductive for long-range TTA and TPQ. Developing well-founded OLED design rules will thus require that more quantitative information is available on the rate and detailed mechanism of the TPQ and TTA processes.

Neural-network-based state feedback control of a nonlinear discrete-time system in nonstrict feedback form.

PubMed

Jagannathan, Sarangapani; He, Pingan

2008-12-01

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

PubMed

Azami, Hamed; Escudero, Javier

2015-08-01

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

Roles of NN-interaction components in shell-structure evolution

NASA Astrophysics Data System (ADS)

Umeya, Atsushi; Muto, Kazuo

2016-11-01

Since the importance of the monopole interaction was first emphasized in 1960s, roles of monopole strengths of two-body nucleon-nucleon interaction in shell structure have been discussed. Through the monopole strengths, we study the roles in shell-structure evolution, starting from explicit forms of the interaction. For the tensor component of the interaction, we show the derivation of the relation, (2j> + 1)Vjj> + (2j< + 1)Vjj< = 0, with a detailed manipulation. We show that one-body spin-orbit term appears in the multipole expansion of two-body spin-orbit interaction. Only the spin-orbit components can affect the spin-orbit energy splitting between spin-orbit partners, when the spin-orbit partner orbits are fully occupied.

All Prime Contract Awards by State or Country, Place, and Contractor, FY 87. Part 4. Sage Hen, California-Yolo, California.

DTIC Science & Technology

1987-01-01

WWU LU @6In 9-a I zo 4 In Nc041l44 - an 0 .)-o4 0 a W: to:3 1~)It 0) U0 f6 -) D0 -’ Cat 1 = NN g -46 6 * 6ii O --.. -4 n O io o o L ~ nn i V)( n L nL...8217 80 I 3 NN N N NN N-CO Nj NN No NN0 N-I N4(o 0 00 0 8-I I6 86M0 t ( ACCA - 0 (f",J- 0 0C 00 ~ 0 Nr0. 004 w - C) V-000 jCl 0 0- 0 004n n-4 c 0Y w1 47

Shape-selective recognition of DNA abasic sites by metallohelices: inhibition of human AP endonuclease 1

PubMed Central

Malina, Jaroslav; Scott, Peter; Brabec, Viktor

2015-01-01

Loss of a base in DNA leading to creation of an abasic (AP) site leaving a deoxyribose residue in the strand, is a frequent lesion that may occur spontaneously or under the action of various physical and chemical agents. Progress in the understanding of the chemistry and enzymology of abasic DNA largely relies upon the study of AP sites in synthetic duplexes. We report here on interactions of diastereomerically pure metallo–helical ‘flexicate’ complexes, bimetallic triple-stranded ferro-helicates [Fe2(NN-NN)3]4+ incorporating the common NN–NN bis(bidentate) helicand, with short DNA duplexes containing AP sites in different sequence contexts. The results show that the flexicates bind to AP sites in DNA duplexes in a shape-selective manner. They preferentially bind to AP sites flanked by purines on both sides and their binding is enhanced when a pyrimidine is placed in opposite orientation to the lesion. Notably, the Λ-enantiomer binds to all tested AP sites with higher affinity than the Δ-enantiomer. In addition, the binding of the flexicates to AP sites inhibits the activity of human AP endonuclease 1, which is as a valid anticancer drug target. Hence, this finding indicates the potential of utilizing well-defined metallo–helical complexes for cancer chemotherapy. PMID:25940617

Effects of partitioned enthalpy of mixing on glass-forming ability

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

Song, Wen-Xiong; Zhao, Shi-Jin, E-mail: shijin.zhao@shu.edu.cn

2015-04-14

We explore the inherent reason at atomic level for the glass-forming ability of alloys by molecular simulation, in which the effect of partitioned enthalpy of mixing is studied. Based on Morse potential, we divide the enthalpy of mixing into three parts: the chemical part (Δ E{sub nn}), strain part (Δ E{sub strain}), and non-bond part (Δ E{sub nnn}). We find that a large negative Δ E{sub nn} value represents strong AB chemical bonding in AB alloy and is the driving force to form a local ordered structure, meanwhile the transformed local ordered structure needs to satisfy the condition (Δ E{submore » nn}/2 + Δ E{sub strain}) < 0 to be stabilized. Understanding the chemical and strain parts of enthalpy of mixing is helpful to design a new metallic glass with a good glass forming ability. Moreover, two types of metallic glasses (i.e., “strain dominant” and “chemical dominant”) are classified according to the relative importance between chemical effect and strain effect, which enriches our knowledge of the forming mechanism of metallic glass. Finally, a soft sphere model is established, different from the common hard sphere model.« less

Molecular evolution and functional characterisation of an ancient phenylalanine ammonia-lyase gene (NnPAL1) from Nelumbo nucifera: novel insight into the evolution of the PAL family in angiosperms

PubMed Central

2014-01-01

Background Phenylalanine ammonia-lyase (PAL; E.C.4.3.1.5) is a key enzyme of the phenylpropanoid pathway in plant development, and it catalyses the deamination of phenylalanine to trans-cinnamic acid, leading to the production of secondary metabolites. This enzyme has been identified in many organisms, ranging from prokaryotes to higher plants. Because Nelumbo nucifera is a basal dicot rich in many secondary metabolites, it is a suitable candidate for research on the phenylpropanoid pathway. Results Three PAL members, NnPAL1, NnPAL2 and NnPAL3, have been identified in N. nucifera using genome-wide analysis. NnPAL1 contains two introns; however, both NnPAL2 and NnPAL3 have only one intron. Molecular and evolutionary analysis of NnPAL1 confirms that it is an ancient PAL member of the angiosperms and may have a different origin. However, PAL clusters, except NnPAL1, are monophyletic after the split between dicots and monocots. These observations suggest that duplication events remain an important occurrence in the evolution of the PAL gene family. Molecular assays demonstrate that the mRNA of the NnPAL1 gene is 2343 bp in size and encodes a 717 amino acid polypeptide. The optimal pH and temperature of the recombinant NnPAL1 protein are 9.0 and 55°C, respectively. The NnPAL1 protein retains both PAL and weak TAL catalytic activities with Km values of 1.07 mM for L-phenylalanine and 3.43 mM for L-tyrosine, respectively. Cis-elements response to environmental stress are identified and confirmed using real-time PCR for treatments with abscisic acid (ABA), indoleacetic acid (IAA), ultraviolet light, Neurospora crassa (fungi) and drought. Conclusions We conclude that the angiosperm PAL genes are not derived from a single gene in an ancestral angiosperm genome; therefore, there may be another ancestral duplication and vertical inheritance from the gymnosperms. The different evolutionary histories for PAL genes in angiosperms suggest different mechanisms of functional regulation. The expression patterns of NnPAL1 in response to stress may be necessary for the survival of N. nucifera since the Cretaceous Period. The discovery and characterisation of the ancient NnPAL1 help to elucidate PAL evolution in angiosperms. PMID:24884360

Near-Neighbor Algorithms for Processing Bearing Data

DTIC Science & Technology

1989-05-10

neighbor algorithms need not be universally more cost -effective than brute force methods. While the data access time of near-neighbor techniques scales with...the number of objects N better than brute force, the cost of setting up the data structure could scale worse than (Continues) 20...for the near neighbors NN2 1 (i). Depending on the particular NN algorithm, the cost of accessing near neighbors for each ai E S1 scales as either N

Induced Hyperon-Nucleon-Nucleon Interactions and the Hyperon Puzzle.

PubMed

Wirth, Roland; Roth, Robert

2016-10-28

We present the first ab initio calculations for p-shell hypernuclei including hyperon-nucleon-nucleon (YNN) contributions induced by a similarity renormalization group transformation of the initial hyperon-nucleon interaction. The transformation including the YNN terms conserves the spectrum of the Hamiltonian while drastically improving model-space convergence of the importance-truncated no-core model, allowing a precise extraction of binding and excitation energies. Results using a hyperon-nucleon interaction at leading order in chiral effective field theory for lower- to mid-p-shell hypernuclei show a good reproduction of experimental excitation energies while hyperon separation energies are typically overestimated. The induced YNN contributions are strongly repulsive and we show that they are related to a decoupling of the Σ hyperons from the hypernuclear system, i.e., a suppression of the Λ-Σ conversion terms in the Hamiltonian. This is linked to the so-called hyperon puzzle in neutron-star physics and provides a basic mechanism for the explanation of strong ΛNN three-baryon forces.

Effect of ploidy on scale-cover pattern in linear ornamental (koi) common carp Cyprinus carpio.

PubMed

Gomelsky, B; Schneider, K J; Glennon, R P; Plouffe, D A

2012-09-01

The effect of ploidy on scale-cover pattern in linear ornamental (koi) common carp Cyprinus carpio was investigated. To obtain diploid and triploid linear fish, eggs taken from a leather C. carpio female (genotype ssNn) and sperm taken from a scaled C. carpio male (genotype SSnn) were used for the production of control (no shock) and heat-shocked progeny. In heat-shocked progeny, the 2 min heat shock (40° C) was applied 6 min after insemination. Diploid linear fish (genotype SsNn) demonstrated a scale-cover pattern typical for this category with one even row of scales along lateral line and few scales located near operculum and at bases of fins. The majority (97%) of triploid linear fish (genotype SssNnn) exhibited non-typical scale patterns which were characterized by the appearance of additional scales on the body. The extent of additional scales in triploid linear fish was variable; some fish had large scales, which covered almost the entire body. Apparently, the observed difference in scale-cover pattern between triploid and diploid linear fish was caused by different phenotypic expression of gene N/n. Due to incomplete dominance of allele N, triploids Nnn demonstrate less profound reduction of scale cover compared with diploids Nn. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

Integrated Sensing Processor, Phase 2

DTIC Science & Technology

2005-12-01

performance analysis for several baseline classifiers including neural nets, linear classifiers, and kNN classifiers. Use of CCDR as a preprocessing step...below the level of the benchmark non-linear classifier for this problem ( kNN ). Furthermore, the CCDR preconditioned kNN achieved a 10% improvement over...the benchmark kNN without CCDR. Finally, we found an important connection between intrinsic dimension estimation via entropic graphs and the optimal

Design of neural network model-based controller in a fed-batch microbial electrolysis cell reactor for bio-hydrogen gas production

NASA Astrophysics Data System (ADS)

Azwar; Hussain, M. A.; Abdul-Wahab, A. K.; Zanil, M. F.; Mukhlishien

2018-03-01

One of major challenge in bio-hydrogen production process by using MEC process is nonlinear and highly complex system. This is mainly due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. Thus, precise control is required for the MEC reactor, so that the amount of current required to produce hydrogen gas can be controlled according to the composition of the substrate in the reactor. In this work, two schemes for controlling the current and voltage of MEC were evaluated. The controllers evaluated are PID and Inverse neural network (NN) controller. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output is based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. The neural network-based controller results in fast response time and less overshoots while the offset effects are minimal. In conclusion, the Inverse neural network (NN)-based controllers provide better control performance for the MEC system compared to the PID controller.

Measurement of the relative yields of ψ ( 2 S ) to ψ ( 1 S ) mesons produced at forward and backward rapidity in p + p , p + Al , p + Au , and He 3 + Au collisions at s NN = 200 GeV

DOE PAGES

Adare, A.; Aidala, C.; Ajitanand, N. N.; ...

2017-03-09

The PHENIX Collaboration has measured the ratio of the yields of ψ(2S) to ψ(1S) mesons produced in p+p, p+Al, p+Au, and 3He+Au collisions at √ sNN = 200 GeV over the forward and backward rapidity intervals 1.2 < |y| < 2.2. We find that the ratio in p+p collisions is consistent with measurements at other collision energies. In collisions with nuclei, we find that in the forward (p-going or 3He-going) direction, the relative yield of ψ(2S) mesons to ψ(1S) mesons is consistent with the value measured in p+p collisions. However, in the backward (nucleus-going) direction, the ψ(2S) meson is preferentiallymore » suppressed by a factor of ~2. This suppression is attributed in some models to the breakup of the weakly bound ψ(2S) meson through final-state interactions with comoving particles, which have a higher density in the nucleus-going direction. As a result, these breakup effects may compete with color screening in a deconfined quark-gluon plasma to produce sequential suppression of excited quarkonia states.« less

Kaon femtoscopy in Pb-Pb collisions at s NN = 2.76 TeV

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

Acharya, S.; Adam, J.; Adamová, D.

Here, we presenmore » t the results of three-dimensional femtoscopic analyses for charged and neutral kaons recorded by ALICE in Pb-Pb collisions at s NN =2.76 TeV. Femtoscopy is used to measure the space-time characteristics of particle production from the effects of quantum statistics and final-state interactions in two-particle correlations. Kaon femtoscopy is an important supplement to that of pions because it allows one to distinguish between different model scenarios working equally well for pions. In particular, we compare the measured three-dimensional kaon radii with a purely hydrodynamical calculation and a model where the hydrodynamic phase is followed by a hadronic rescattering stage. The former predicts an approximate transverse mass (m T) scaling of source radii obtained from pion and kaon correlations. This m T scaling appears to be broken in our data, which indicates the importance of the hadronic rescattering phase at LHC energies. A k T scaling of pion and kaon source radii is observed instead. The time of maximal emission of the system is estimated by using the three-dimensional femtoscopic analysis for kaons. The measured emission time is larger than that of pions. Our observation is well supported by the hydrokinetic model predictions.« less

Measurement of the relative yields of ψ ( 2 S ) to ψ ( 1 S ) mesons produced at forward and backward rapidity in p + p , p + Al , p + Au , and He 3 + Au collisions at s NN = 200 GeV

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

Adare, A.; Aidala, C.; Ajitanand, N. N.

The PHENIX Collaboration has measured the ratio of the yields of ψ(2S) to ψ(1S) mesons produced in p+p, p+Al, p+Au, and 3He+Au collisions at √ sNN = 200 GeV over the forward and backward rapidity intervals 1.2 < |y| < 2.2. We find that the ratio in p+p collisions is consistent with measurements at other collision energies. In collisions with nuclei, we find that in the forward (p-going or 3He-going) direction, the relative yield of ψ(2S) mesons to ψ(1S) mesons is consistent with the value measured in p+p collisions. However, in the backward (nucleus-going) direction, the ψ(2S) meson is preferentiallymore » suppressed by a factor of ~2. This suppression is attributed in some models to the breakup of the weakly bound ψ(2S) meson through final-state interactions with comoving particles, which have a higher density in the nucleus-going direction. As a result, these breakup effects may compete with color screening in a deconfined quark-gluon plasma to produce sequential suppression of excited quarkonia states.« less

Kaon femtoscopy in Pb-Pb collisions at √{sNN}=2.76 TeV

NASA Astrophysics Data System (ADS)

Acharya, S.; Adam, J.; Adamová, D.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, N.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Alba, J. L. B.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altenkamper, L.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andreou, D.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C.; Antičić, T.; Antinori, F.; Antonioli, P.; Anwar, R.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Ball, M.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barioglio, L.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Batigne, G.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Blair, J. T.; Blau, D.; Blume, C.; Boca, G.; Bock, F.; Bogdanov, A.; Boldizsár, L.; Bombara, M.; Bonomi, G.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Botta, E.; Bourjau, C.; Bratrud, L.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Capon, A. A.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cerello, P.; Chandra, S.; Chang, B.; Chapeland, S.; Chartier, M.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Chojnacki, M.; Choudhury, S.; Chowdhury, T.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Concas, M.; Conesa Balbastre, G.; Conesa Del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Costanza, S.; Crkovská, J.; Crochet, P.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; de, S.; de Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; de Falco, A.; de Gruttola, D.; De Marco, N.; de Pasquale, S.; de Souza, R. D.; Degenhardt, H. F.; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; di Bari, D.; di Mauro, A.; di Nezza, P.; di Ruzza, B.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Doremalen, L. V. V.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Fabbietti, L.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Ganoti, P.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Gay Ducati, M. B.; Germain, M.; Ghosh, J.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Greiner, L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grion, N.; Gronefeld, J. M.; Grosa, F.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Haque, M. R.; Harris, J. W.; Harton, A.; Hassan, H.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hills, C.; Hippolyte, B.; Hladky, J.; Hohlweger, B.; Horak, D.; Hornung, S.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Iga Buitron, S. A.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Irfan, M.; Islam, M. S.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovsky, J.; Jaelani, S.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jercic, M.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karczmarczyk, P.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Ketzer, B.; Khabanova, Z.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kielbowicz, M. M.; Kileng, B.; Kim, B.; Kim, D.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Konyushikhin, M.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lai, Y. S.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lavicka, R.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Lévai, P.; Li, X.; Lien, J.; Lietava, R.; Lim, B.; Lindal, S.; Lindenstruth, V.; Lindsay, S. W.; Lippmann, C.; Lisa, M. A.; Litichevskyi, V.; Llope, W. J.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Loncar, P.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Luhder, J. R.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martinez, J. A. L.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Masson, E.; Mastroserio, A.; Mathis, A. M.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mihaylov, D. L.; Mikhaylov, K.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Mohisin Khan, M.; Montes, E.; Moreira de Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Myers, C. J.; Myrcha, J. W.; Nag, D.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Narayan, A.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao de Oliveira, R. A.; Nellen, L.; Nesbo, S. V.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Ohlson, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Pachmayer, Y.; Pacik, V.; Pagano, D.; Pagano, P.; Paić, G.; Palni, P.; Pan, J.; Pandey, A. K.; Panebianco, S.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Parmar, S.; Passfeld, A.; Pathak, S. P.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira, L. G.; Pereira da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Pezzi, R. P.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pliquett, F.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Pozdniakov, V.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Rana, D. B.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Rokita, P. S.; Ronchetti, F.; Rosas, E. D.; Rosnet, P.; Rossi, A.; Rotondi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rueda, O. V.; Rui, R.; Rumyantsev, B.; Rustamov, A.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Saha, S. K.; Sahlmuller, B.; Sahoo, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandoval, A.; Sarkar, D.; Sarkar, N.; Sarma, P.; Sas, M. H. P.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Scheid, H. S.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M. O.; Schmidt, M.; Schmidt, N. V.; Schuchmann, S.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sett, P.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shahoyan, R.; Shaikh, W.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Stocco, D.; Storetvedt, M. M.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thakur, D.; Thakur, S.; Thomas, D.; Thoresen, F.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Torres, S. R.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Tropp, L.; Trubnikov, V.; Trzaska, W. H.; Trzeciak, B. A.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Umaka, E. N.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; van der Maarel, J.; van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Wagner, B.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wenzel, S. C.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Willsher, E.; Windelband, B.; Witt, W. E.; Yalcin, S.; Yamakawa, K.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.; Zou, S.; Alice Collaboration

2017-12-01

We present the results of three-dimensional femtoscopic analyses for charged and neutral kaons recorded by ALICE in Pb-Pb collisions at √{sNN}=2.76 TeV. Femtoscopy is used to measure the space-time characteristics of particle production from the effects of quantum statistics and final-state interactions in two-particle correlations. Kaon femtoscopy is an important supplement to that of pions because it allows one to distinguish between different model scenarios working equally well for pions. In particular, we compare the measured three-dimensional kaon radii with a purely hydrodynamical calculation and a model where the hydrodynamic phase is followed by a hadronic rescattering stage. The former predicts an approximate transverse mass (mT) scaling of source radii obtained from pion and kaon correlations. This mT scaling appears to be broken in our data, which indicates the importance of the hadronic rescattering phase at LHC energies. A kT scaling of pion and kaon source radii is observed instead. The time of maximal emission of the system is estimated by using the three-dimensional femtoscopic analysis for kaons. The measured emission time is larger than that of pions. Our observation is well supported by the hydrokinetic model predictions.

Measurement of the relative yields of ψ (2 S ) to ψ (1 S ) mesons produced at forward and backward rapidity in p +p , p +Al , p +Au , and 3He+Au collisions at √{sNN}=200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Alfred, M.; Andrieux, V.; Aoki, K.; Apadula, N.; Asano, H.; Ayuso, C.; Azmoun, B.; Babintsev, V.; Bai, M.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Bathe, S.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belmont, R.; Berdnikov, A.; Berdnikov, Y.; Blau, D. S.; Boer, M.; Bok, J. S.; Bownes, E. K.; Boyle, K.; Brooks, M. L.; Bryslawskyj, J.; Bumazhnov, V.; Butler, C.; Campbell, S.; Canoa Roman, V.; Cervantes, R.; Chen, C.-H.; Chi, C. Y.; Chiu, M.; Choi, I. J.; Choi, J. B.; Chujo, T.; Citron, Z.; Connors, M.; Cronin, N.; Csanád, M.; Csörgő, T.; Danley, T. W.; Datta, A.; Daugherity, M. S.; David, G.; Deblasio, K.; Dehmelt, K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Dion, A.; Diss, P. B.; Dixit, D.; Do, J. H.; Drees, A.; Drees, K. A.; Dumancic, M.; Durham, J. M.; Durum, A.; Dusing, J. P.; Elder, T.; Enokizono, A.; En'yo, H.; Esumi, S.; Fadem, B.; Fan, W.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fokin, S. L.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fukuda, Y.; Gal, C.; Gallus, P.; Garg, P.; Ge, H.; Giordano, F.; Glenn, A.; Goto, Y.; Grau, N.; Greene, S. V.; Grosse Perdekamp, M.; Gunji, T.; Guragain, H.; Hachiya, T.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Hamilton, H. F.; Han, S. Y.; Hanks, J.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; He, X.; Hemmick, T. K.; Hill, J. C.; Hill, K.; Hollis, R. S.; Homma, K.; Hong, B.; Hoshino, T.; Hotvedt, N.; Huang, J.; Huang, S.; Imai, K.; Imrek, J.; Inaba, M.; Iordanova, A.; Isenhower, D.; Ito, Y.; Ivanishchev, D.; Jacak, B. V.; Jezghani, M.; Ji, Z.; Jia, J.; Jiang, X.; Johnson, B. M.; Jorjadze, V.; Jouan, D.; Jumper, D. S.; Kanda, S.; Kang, J. H.; Kapukchyan, D.; Karthas, S.; Kawall, D.; Kazantsev, A. V.; Key, J. A.; Khachatryan, V.; Khanzadeev, A.; Kim, C.; Kim, D. J.; Kim, E.-J.; Kim, G. W.; Kim, M.; Kimball, M. L.; Kimelman, B.; Kincses, D.; Kistenev, E.; Kitamura, R.; Klatsky, J.; Kleinjan, D.; Kline, P.; Koblesky, T.; Komkov, B.; Kotler, J. R.; Kotov, D.; Kudo, S.; Kurita, K.; Kurosawa, M.; Kwon, Y.; Lacey, R.; Lajoie, J. G.; Lallow, E. O.; Lebedev, A.; Lee, S.; Lee, S. H.; Leitch, M. J.; Leung, Y. H.; Lewis, N. A.; Li, X.; Li, X.; Lim, S. H.; Liu, L. D.; Liu, M. X.; Loggins, V.-R.; Loggins, V.-R.; Lovasz, K.; Lynch, D.; Majoros, T.; Makdisi, Y. I.; Makek, M.; Malaev, M.; Manion, A.; Manko, V. I.; Mannel, E.; Masuda, H.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Meles, A.; Mendez, A. R.; Mendoza, M.; Mignerey, A. C.; Mihalik, D. E.; Milov, A.; Mishra, D. K.; Mitchell, J. T.; Mitsuka, G.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Montuenga, P.; Moon, T.; Morrison, D. P.; Morrow, S. I. M.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Mwai, A.; Nagai, K.; Nagashima, K.; Nagashima, T.; Nagle, J. L.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakano, K.; Nattrass, C.; Netrakanti, P. K.; Niida, T.; Nishimura, S.; Nouicer, R.; Novák, T.; Novitzky, N.; Novotny, R.; Nyanin, A. S.; O'Brien, E.; Ogilvie, C. A.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Ottino, G. J.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, J. S.; Park, S.; Pate, S. F.; Patel, M.; Peng, J.-C.; Peng, W.; Perepelitsa, D. V.; Perera, G. D. N.; Peressounko, D. Yu.; Perezlara, C. E.; Perry, J.; Petti, R.; Phipps, M.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Press, C. J.; Pun, A.; Purschke, M. L.; Rak, J.; Ramson, B. J.; Ravinovich, I.; Read, K. F.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richford, D.; Rinn, T.; Rolnick, S. D.; Rosati, M.; Rowan, Z.; Rubin, J. G.; Runchey, J.; Safonov, A. S.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sako, H.; Samsonov, V.; Sarsour, M.; Sato, K.; Sato, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seidl, R.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shein, I.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shioya, T.; Shukla, P.; Sickles, A.; Silva, C. L.; Silva, J. A.; Silvermyr, D.; Singh, B. K.; Singh, C. P.; Singh, V.; Slunečka, M.; Smith, K. L.; Snowball, M.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Stankus, P. W.; Stepanov, M.; Stien, H.; Stoll, S. P.; Sugitate, T.; Sukhanov, A.; Sumita, T.; Sun, J.; Syed, S.; Sziklai, J.; Takeda, A.; Taketani, A.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tarnai, G.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Tomášek, M.; Towell, C. L.; Towell, R.; Towell, R. S.; Tserruya, I.; Ueda, Y.; Ujvari, B.; van Hecke, H. W.; Vazquez-Carson, S.; Velkovska, J.; Virius, M.; Vrba, V.; Vukman, N.; Wang, X. R.; Wang, Z.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; White, A. S.; Wong, C. P.; Woody, C. L.; Wysocki, M.; Xia, B.; Xu, C.; Xu, Q.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yamamoto, H.; Yanovich, A.; Yin, P.; Yoo, J. H.; Yoon, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zelenski, A.; Zharko, S.; Zhou, S.; Zou, L.; Phenix Collaboration

2017-03-01

The PHENIX Collaboration has measured the ratio of the yields of ψ (2 S ) to ψ (1 S ) mesons produced in p +p , p +Al , p +Au , and 3He+Au collisions at √{s NN}=200 GeV over the forward and backward rapidity intervals 1.2 <|y |<2.2 . We find that the ratio in p +p collisions is consistent with measurements at other collision energies. In collisions with nuclei, we find that in the forward (p -going or 3He-going) direction, the relative yield of ψ (2 S ) mesons to ψ (1 S ) mesons is consistent with the value measured in p +p collisions. However, in the backward (nucleus-going) direction, the ψ (2 S ) meson is preferentially suppressed by a factor of ˜2 . This suppression is attributed in some models to the breakup of the weakly bound ψ (2 S ) meson through final-state interactions with comoving particles, which have a higher density in the nucleus-going direction. These breakup effects may compete with color screening in a deconfined quark-gluon plasma to produce sequential suppression of excited quarkonia states.

Neutron-Proton Scattering Experiments at ANKE-COSY

NASA Astrophysics Data System (ADS)

Kacharava, A.; Chiladze, D.; Chiladze, B.; Keshelashvili, I.; Lomidze, N.; Macharashvili, G.; McHedlishvili, D.; Nioradze, M.; Rathmann, F.; Ströher, H.; Wilkin, C.

2010-04-01

The nucleon-nucleon interaction (NN) is fundamental for the whole of nuclear physics and hence to the composition of matter as we know it. It has been demonstrated that stored, polarised beams and polarised internal targets are experimental tools of choice to probe spin effects in NN-scattering experiments. While the EDDA experiment has dramatically improved the proton-proton date base, information on spin observables in neutron-proton scattering is very incomplete above 800 MeV, resulting in large uncertainties in isoscalar n p phase shifts. Experiments at COSY, using a polarised deuteron beam or target, can lead to significant improvements in the situation through the study of quasi-free reactions on the neutron in the deuteron. Such a measurements has already been started at ANKE by using polarised deuterons on an unpolarised target to study the dp → ppn deuteron charge-exchange reaction and the full program with a polarised storage cell target just has been conducted. At low excitation energies of the final pp system, the spin observables are directly related to the spin- dependent parts of the neutron-proton charge-exchange amplitudes. Our measurement of the deuteron-proton spin correlations will allow us to determine the relative phases of these amplitudes in addition to their overall magnitudes.

Kaon femtoscopy in Pb-Pb collisions at s NN = 2.76 TeV

DOE PAGES

Acharya, S.; Adam, J.; Adamová, D.; ...

2017-12-21

Here, we presenmore » t the results of three-dimensional femtoscopic analyses for charged and neutral kaons recorded by ALICE in Pb-Pb collisions at s NN =2.76 TeV. Femtoscopy is used to measure the space-time characteristics of particle production from the effects of quantum statistics and final-state interactions in two-particle correlations. Kaon femtoscopy is an important supplement to that of pions because it allows one to distinguish between different model scenarios working equally well for pions. In particular, we compare the measured three-dimensional kaon radii with a purely hydrodynamical calculation and a model where the hydrodynamic phase is followed by a hadronic rescattering stage. The former predicts an approximate transverse mass (m T) scaling of source radii obtained from pion and kaon correlations. This m T scaling appears to be broken in our data, which indicates the importance of the hadronic rescattering phase at LHC energies. A k T scaling of pion and kaon source radii is observed instead. The time of maximal emission of the system is estimated by using the three-dimensional femtoscopic analysis for kaons. The measured emission time is larger than that of pions. Our observation is well supported by the hydrokinetic model predictions.« less

Mean Conditions and Turbulence Statistics at Vandenberg AFB, California,

DTIC Science & Technology

1984-11-01

2NN M N NN ’CN N NN N4 I’~tJN NN NIJN I pIi JN’ W’N NCJ’ ViNNCJ m I’N’(I N’N CJ N N N C’ 0 ’o CD22 REPRODUCEDU AT CG0VFRr4?A.FJ1 uvrF 11.4 PiJ0t’ N

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Zhang, Huaguang; Lin, Chong

2016-01-01

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

Determination of the geophysical model function of the ERS-1 scatterometer by the use of neural networks

NASA Astrophysics Data System (ADS)

Mejia, Carlos; Thiria, Sylvie; Tran, Ngan; CréPon, Michel; Badran, Fouad

1998-06-01

We present a geophysical model function (GMF) for the ERS-1 scatterometer computed by the use of neural networks. The neural networks GMF (NN GMF) is calibrated with ERS-1 scatterometer sigma 0 collocated with European Center for Medium-Range Weather Forecasts (ECMWF) analyzed wind vectors. Four different NN GMFs have been computed: one for each antenna and an average NN GMF. These NN GMFs do not present any significant differences which means that the three antenna are quasi-identical. The NN GMFs exhibit a biharmonic dependence on the wind azimuth with a small upwind-downwind modulation as found on previous GMFs. In order to check the validity of the NN GMF systematic comparisons with the European Space Agency (ESA) C band model (CMOD4) GMF (version 2 of March 25, 1993) and the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) CMOD213 GMF are done. It is found that the NN GMFs are highly accurate and relevant functions to model the ERS-1 scatterometer sigma 0.

Jet properties in PbPb and pp collisions at √{s_{NN}}=5.02 TeV

NASA Astrophysics Data System (ADS)

Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Escalante Del Valle, A.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hrubec, J.; Jeitler, M.; Krammer, N.; Krätschmer, I.; Liko, D.; Madlener, T.; Mikulec, I.; Rad, N.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Spanring, M.; Spitzbart, D.; Taurok, A.; Waltenberger, W.; Wittmann, J.; Wulz, C.-E.; Zarucki, M.; Chekhovsky, V.; Mossolov, V.; Suarez Gonzalez, J.; De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Pieters, M.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I.; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Marchesini, I.; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Kalsi, A. 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S.; Lee, J.; Lee, S.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Kim, H.; Moon, D. H.; Oh, G.; Brochero Cifuentes, J. A.; Goh, J.; Kim, T. J.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Almond, J.; Kim, J.; Kim, J. S.; Lee, H.; Lee, K.; Nam, K.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. h.; Yang, U. K.; Yoo, H. D.; Yu, G. B.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Choi, Y.; Hwang, C.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Yusli, M. N.; Zolkapli, Z.; Reyes-Almanza, R.; Ramirez-Sanchez, G.; Duran-Osuna, M. C.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Rabadan-Trejo, R. I.; Lopez-Fernandez, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.; Eysermans, J.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Saddique, A.; Shah, M. A.; Shoaib, M.; Waqas, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Szleper, M.; Traczyk, P.; Zalewski, P.; Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Pyskir, A.; Walczak, M.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Galinhas, B.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M. V.; Seixas, J.; Strong, G.; Toldaiev, O.; Vadruccio, D.; Varela, J.; Afanasiev, S.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Voytishin, N.; Zarubin, A.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sosnov, D.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Stepennov, A.; Stolin, V.; Toms, M.; Vlasov, E.; Zhokin, A.; Aushev, T.; Bylinkin, A.; Chadeeva, M.; Parygin, P.; Philippov, D.; Polikarpov, S.; Popova, E.; Rusinov, V.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Rusakov, S. V.; Terkulov, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Ershov, A.; Gribushin, A.; Kaminskiy, A.; Kodolova, O.; Korotkikh, V.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.; Vardanyan, I.; Blinov, V.; Shtol, D.; Skovpen, Y.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Godizov, A.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Mandrik, P.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Babaev, A.; Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.; Alcaraz Maestre, J.; Bachiller, I.; Barrio Luna, M.; Cerrada, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Moran, D.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Redondo, I.; Romero, L.; Soares, M. S.; Triossi, A.; Álvarez Fernández, A.; Albajar, C.; de Trocóniz, J. F.; Cuevas, J.; Erice, C.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; González Fernández, J. R.; Palencia Cortezon, E.; Sanchez Cruz, S.; Vischia, P.; Vizan Garcia, J. M.; Cabrillo, I. J.; Calderon, A.; Chazin Quero, B.; Duarte Campderros, J.; Fernandez, M.; Fernández Manteca, P. J.; Garcia-Ferrero, J.; García Alonso, A.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Martinez Ruiz del Arbol, P.; Matorras, F.; Piedra Gomez, J.; Prieels, C.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Akgun, B.; Auffray, E.; Baillon, P.; Ball, A. H.; Barney, D.; Bendavid, J.; Bianco, M.; Bocci, A.; Botta, C.; Camporesi, T.; Cepeda, M.; Cerminara, G.; Chapon, E.; Chen, Y.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Roeck, A.; Deelen, N.; Dobson, M.; du Pree, T.; Dünser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Fallavollita, F.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gilbert, A.; Gill, K.; Glege, F.; Gulhan, D.; Hegeman, J.; Innocente, V.; Jafari, A.; Janot, P.; Karacheban, O.; Kieseler, J.; Knünz, V.; Kornmayer, A.; Krammer, M.; Lange, C.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Malgeri, L.; Mannelli, M.; Martelli, A.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Mulders, M.; Neugebauer, H.; Ngadiuba, J.; Orfanelli, S.; Orsini, L.; Pantaleo, F.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pitters, F. M.; Rabady, D.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Seidel, M.; Selvaggi, M.; Sharma, A.; Silva, P.; Sphicas, P.; Stakia, A.; Steggemann, J.; Stoye, M.; Tosi, M.; Treille, D.; Tsirou, A.; Veckalns, V.; Verweij, M.; Zeuner, W. D.; Bertl, W.; Caminada, L.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Wiederkehr, S. A.; Backhaus, M.; Bäni, L.; Berger, P.; Casal, B.; Chernyavskaya, N.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dorfer, C.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Klijnsma, T.; Lustermann, W.; Marionneau, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; NessiTedaldi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Reichmann, M.; Ruini, D.; Sanz Becerra, D. A.; Schönenberger, M.; Shchutska, L.; Tavolaro, V. R.; Theofilatos, K.; Vesterbacka Olsson, M. L.; Wallny, R.; Zhu, D. H.; Aarrestad, T. K.; Amsler, C.; Brzhechko, D.; Canelli, M. F.; De Cosa, A.; Del Burgo, R.; Donato, S.; Galloni, C.; Hreus, T.; Kilminster, B.; Neutelings, I.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Schweiger, K.; Seitz, C.; Takahashi, Y.; Zucchetta, A.; Chang, Y. H.; Cheng, K. y.; Doan, T. H.; Jain, Sh.; Khurana, R.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Fiori, F.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Paganis, E.; Psallidas, A.; Steen, A.; Tsai, J. f.; Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.; Bakirci, M. N.; Bat, A.; Boran, F.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Eskut, E.; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I.; Kangal, E. E.; Kara, O.; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sunar Cerci, D.; Tok, U. G.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.; Karapinar, G.; Ocalan, K.; Yalvac, M.; Zeyrek, M.; Atakisi, I. O.; Gülmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.; Agaras, M. N.; Atay, S.; Cakir, A.; Cankocak, K.; Komurcu, Y.; Grynyov, B.; Levchuk, L.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Davignon, O.; Flacher, H.; Goldstein, J.; Heath, G. P.; Heath, H. F.; Kreczko, L.; Newbold, D. M.; Paramesvaran, S.; Sakuma, T.; Seif El Nasr-storey, S.; Smith, D.; Smith, V. J.; Belyaev, A.; Brew, C.; Brown, R. M.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Linacre, J.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Womersley, W. J.; Auzinger, G.; Bainbridge, R.; Bloch, P.; Borg, J.; Breeze, S.; Buchmuller, O.; Bundock, A.; Casasso, S.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; Della Negra, M.; Di Maria, R.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Komm, M.; Lane, R.; Laner, C.; Lyons, L.; Magnan, A.-M.; Malik, S.; Mastrolorenzo, L.; Matsushita, T.; Nash, J.; Nikitenko, A.; Palladino, V.; Pesaresi, M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Strebler, T.; Summers, S.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Wardle, N.; Winterbottom, D.; Wright, J.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Morton, A.; Reid, I. D.; Teodorescu, L.; Zahid, S.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Smith, C.; Bartek, R.; Dominguez, A.; Buccilli, A.; Cooper, S. I.; Henderson, C.; Rumerio, P.; West, C.; Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.; Benelli, G.; Cutts, D.; Hadley, M.; Hakala, J.; Heintz, U.; Hogan, J. M.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Lee, J.; Mao, Z.; Narain, M.; Pazzini, J.; Piperov, S.; Sagir, S.; Syarif, R.; Yu, D.; Band, R.; Brainerd, C.; Breedon, R.; Burns, D.; Calderon De La Barca Sanchez, M.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Smith, J.; Stolp, D.; Taylor, D.; Tos, K.; Tripathi, M.; Wang, Z.; Zhang, F.; Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Regnard, S.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Bouvier, E.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Ghiasi Shirazi, S. M. A.; Hanson, G.; Karapostoli, G.; Kennedy, E.; Lacroix, F.; Long, O. R.; Olmedo Negrete, M.; Paneva, M. I.; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Gilbert, D.; Hashemi, B.; Holzner, A.; Klein, D.; Kole, G.; Krutelyov, V.; Letts, J.; Masciovecchio, M.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Wood, J.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Amin, N.; Bhandari, R.; Bradmiller-Feld, J.; Campagnari, C.; Citron, M.; Dishaw, A.; Dutta, V.; Franco Sevilla, M.; Gouskos, L.; Heller, R.; Incandela, J.; Ovcharova, A.; Qu, H.; Richman, J.; Stuart, D.; Suarez, I.; Yoo, J.; Anderson, D.; Bornheim, A.; Bunn, J.; Lawhorn, J. M.; Newman, H. B.; Nguyen, T. Q.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Wilkinson, R.; Xie, S.; Zhang, Z.; Zhu, R. Y.; Andrews, M. B.; Ferguson, T.; Mudholkar, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Weinberg, M.; Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Leontsinis, S.; MacDonald, E.; Mulholland, T.; Stenson, K.; Ulmer, K. A.; Wagner, S. R.; Alexander, J.; Chaves, J.; Cheng, Y.; Chu, J.; Datta, A.; Mcdermott, K.; Mirman, N.; Patterson, J. R.; Quach, D.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Tan, S. M.; Tao, Z.; Thom, J.; Tucker, J.; Wittich, P.; Zientek, M.; Abdullin, S.; Albrow, M.; Alyari, M.; Apollinari, G.; Apresyan, A.; Apyan, A.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Canepa, A.; Cerati, G. B.; Cheung, H. W. K.; Chlebana, F.; Cremonesi, M.; Duarte, J.; Elvira, V. D.; Freeman, J.; Gecse, Z.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kortelainen, M. J.; Kreis, B.; Lammel, S.; Lincoln, D.; Lipton, R.; Liu, M.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Magini, N.; Marraffino, J. M.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Ristori, L.; Savoy-Navarro, A.; Schneider, B.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strait, J.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Wu, W.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Field, R. D.; Furic, I. K.; Gleyzer, S. V.; Joshi, B. M.; Konigsberg, J.; Korytov, A.; Kotov, K.; Ma, P.; Matchev, K.; Mei, H.; Mitselmakher, G.; Shi, K.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Joshi, Y. R.; Linn, S.; Markowitz, P.; Rodriguez, J. L.; Ackert, A.; Adams, T.; Askew, A.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Kolberg, T.; Martinez, G.; Perry, T.; Prosper, H.; Saha, A.; Santra, A.; Sharma, V.; Yohay, R.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Cavanaugh, R.; Chen, X.; Dittmer, S.; Evdokimov, O.; Gerber, C. E.; Hangal, D. A.; Hofman, D. J.; Jung, K.; Kamin, J.; Sandoval Gonzalez, I. D.; Tonjes, M. B.; Varelas, N.; Wang, H.; Wang, X.; Wu, Z.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Hung, W. T.; Maksimovic, P.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; You, C.; Al-bataineh, A.; Baringer, P.; Bean, A.; Benitez, J. F.; Boren, S.; Bowen, J.; Castle, J.; Khalil, S.; Kropivnitskaya, A.; Majumder, D.; Mcbrayer, W.; Murray, M.; Rogan, C.; Royon, C.; Sanders, S.; Schmitz, E.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Maravin, Y.; Modak, A.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Rebassoo, F.; Wright, D.; Baden, A.; Baron, O.; Belloni, A.; Eno, S. C.; Feng, Y.; Ferraioli, C.; Hadley, N. J.; Jabeen, S.; Jeng, G. Y.; Kellogg, R. G.; Kunkle, J.; Mignerey, A. C.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Tonwar, S. C.; Abercrombie, D.; Allen, B.; Azzolini, V.; Barbieri, R.; Baty, A.; Bauer, G.; Bi, R.; Brandt, S.; Busza, W.; Cali, I. A.; D'Alfonso, M.; Demiragli, Z.; Gomez Ceballos, G.; Goncharov, M.; Harris, P.; Hsu, D.; Hu, M.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Stephans, G. S. F.; Sumorok, K.; Tatar, K.; Velicanu, D.; Wang, J.; Wang, T. W.; Wyslouch, B.; Zhaozhong, S.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Kalafut, S.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Turkewitz, J.; Wadud, M. A.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Claes, D. R.; Fangmeier, C.; Golf, F.; Gonzalez Suarez, R.; Kamalieddin, R.; Kravchenko, I.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Godshalk, A.; Harrington, C.; Iashvili, I.; Nguyen, D.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Freer, C.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Orimoto, T.; Teixeira De Lima, R.; Wamorkar, T.; Wang, B.; Wisecarver, A.; Wood, D.; Bhattacharya, S.; Charaf, O.; Hahn, K. A.; Mucia, N.; Odell, N.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Bucci, R.; Dev, N.; Hildreth, M.; Hurtado Anampa, K.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Li, W.; Loukas, N.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Siddireddy, P.; Smith, G.; Taroni, S.; Wayne, M.; Wightman, A.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Ji, W.; Ling, T. Y.; Luo, W.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Higginbotham, S.; Kalogeropoulos, A.; Lange, D.; Luo, J.; Marlow, D.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroué, P.; Salfeld-Nebgen, J.; Stickland, D.; Tully, C.; Malik, S.; Norberg, S.; Barker, A.; Barnes, V. E.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Khatiwada, A.; Miller, D. H.; Neumeister, N.; Peng, C. C.; Qiu, H.; Schulte, J. F.; Sun, J.; Wang, F.; Xiao, R.; Xie, W.; Cheng, T.; Dolen, J.; Parashar, N.; Chen, Z.; Ecklund, K. M.; Freed, S.; Geurts, F. J. M.; Guilbaud, M.; Kilpatrick, M.; Li, W.; Michlin, B.; Padley, B. P.; Roberts, J.; Rorie, J.; Shi, W.; Tu, Z.; Zabel, J.; Zhang, A.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Ciesielski, R.; Goulianos, K.; Mesropian, C.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Ruiz Alvarez, J. D.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Wang, Y.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Poudyal, N.; Sturdy, J.; Thapa, P.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Carlsmith, D.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Rekovic, V.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Woods, N.

2018-05-01

Modifications of the properties of jets in PbPb collisions, relative to those in pp collisions, are studied at a nucleon-nucleon center-of-mass energy of √{s_{NN}}=5.02 TeV via correlations of charged particles with the jet axis in relative pseudorapidity (Δ η), relative azimuth (Δ ϕ), and relative angular distance from the jet axis Δ r=√{(Δ η )^2+{(Δ φ )}^2} . This analysis uses data collected with the CMS detector at the LHC, corresponding to integrated luminosities of 404 μb-1 and 27.4 pb-1 for PbPb and pp collisions, respectively. Charged particle number densities, jet fragmentation functions, and jet shapes are presented as a function of PbPb collision centrality and charged-particle track transverse momentum, providing a differential description of jet modifications due to interactions with the quark-gluon plasma. [Figure not available: see fulltext.

Observation of D 0 meson nuclear modifications in Au + Au collisions at s NN = 200 GeV

DOE PAGES

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; ...

2014-09-30

We report the first measurement of charmed-hadron (D 0) production via the hadronic decay channel (D 0→K -+π +) in Au+Au collisions at √ sNN=200 GeV with the STAR experiment. The charm production cross section per nucleon-nucleon collision at midrapidity scales with the number of binary collisions, N bin, from p+p to central Au+Au collisions. The D 0 meson yields in central Au+Aucollisions are strongly suppressed compared to those in p+p scaled by N bin, for transverse momenta p T>3 GeV/c, demonstrating significant energy loss of charm quarks in the hot and dense medium. An enhancement at intermediate p Tmore » is also observed. Model calculations including strong charm-medium interactions and coalescence hadronization describe our measurements.« less

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

Predictions for cold nuclear matter effects in p+Pb collisions at √{sNN } = 8.16 TeV

NASA Astrophysics Data System (ADS)

Albacete, Javier L.; Arleo, François; Barnaföldi, Gergely G.; Bíró, Gábor; d'Enterria, David; Ducloué, Bertrand; Eskola, Kari J.; Ferreiro, Elena G.; Gyulassy, Miklos; Harangozó, Szilvester Miklós; Helenius, Ilkka; Kang, Zhong-Bo; Kotko, Piotr; Kulagin, Sergey A.; Kutak, Krzysztof; Lansberg, Jean Philippe; Lappi, Tuomas; Lévai, Péter; Lin, Zi-Wei; Ma, Guoyang; Ma, Yan-Qing; Mäntysaari, Heikki; Paukkunen, Hannu; Papp, Gábor; Petti, Roberto; Rezaeian, Amir H.; Ru, Peng; Sapeta, Sebastian; Schenke, Björn; Schlichting, Sören; Shao, Hua-Sheng; Tribedy, Prithwish; Venugopalan, Raju; Vitev, Ivan; Vogt, Ramona; Wang, Enke; Wang, Xin-Nian; Xing, Hongxi; Xu, Rong; Zhang, Ben-Wei; Zhang, Hong-Fei; Zhang, Wei-Ning

2018-04-01

Predictions for cold nuclear matter effects on charged hadrons, identified light hadrons, quarkonium and heavy flavor hadrons, Drell-Yan dileptons, jets, photons, gauge bosons and top quark pairs produced in p+Pb collisions at √{sNN } = 8.16 TeV are compiled and, where possible, compared to each other. Predictions of the normalized ratios of p+Pb to p + p cross sections are also presented for most of the observables, providing new insights into the expected role of cold nuclear matter effects. In particular, the role of nuclear parton distribution functions on particle production can now be probed over a wider range of phase space than ever before.

A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging

PubMed Central

Kwon, Oh-Hun; Park, Hyunjin; Seo, Sang-Won; Na, Duk L.; Lee, Jong-Min

2015-01-01

The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface, considering the anatomical correspondence between the DTI and the T1-weighted image. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. Here we introduce a Surface Guided Diffusion Mapping (SGDM) method to compensate for such geometric distortions. We compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. We also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The CSF probability values provided by the SGDM method were lower than those produced by the NN and Linear methods. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the NN and Linear methods assigned the MD value in the CSF region to the cortical midsurface (GM region). Our results suggest that the SGDM method is an effective way to correct such mapping errors. PMID:26236180

Prediction of Classroom Reverberation Time using Neural Network

NASA Astrophysics Data System (ADS)

Liyana Zainudin, Fathin; Kadir Mahamad, Abd; Saon, Sharifah; Nizam Yahya, Musli

2018-04-01

In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE < 0.005 and R > 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds.

Iowa State University – Final Report for SciDAC3/NUCLEI

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

Vary, James P

The Iowa State University (ISU) contributions to the NUCLEI project are focused on developing, implementing and running an efficient and scalable configuration interaction code (Many-Fermion Dynamics – nuclear or MFDn) for leadership class supercomputers addressing forefront research problems in low-energy nuclear physics. We investigate nuclear structure and reactions with realistic nucleon-nucleon (NN) and three-nucleon (3N) interactions. We select a few highlights from our work that has produced a total of more than 82 refereed publications and more than 109 invited talks under SciDAC3/NUCLEI.

Future of the beam energy scan program at RHIC

NASA Astrophysics Data System (ADS)

Odyniec, Grazyna

2015-05-01

The first exploratory phase of a very successful Beam Energy Scan Program at RHIC was completed in 2014 with Au+Au collisions at energies ranging from 7 to 39 GeV. Data sets taken earlier extended the upper limit of energy range to the √sNN of 200 GeV. This provided an initial look into the uncharted territory of the QCD phase diagram, which is considered to be the single most important graph of our field. The main results from BES phase I, although effected by large statistical errors (steeply increasing with decreasing energy), suggest that the highest potential for discovery of the QCD Critical Point lies bellow √sNN 20 GeV. Here, we discuss the plans and the preparation for phase II of the BES program, with an order of magnitude larger statistics, which is planned for 2018-2019. The BES II will focus on Au+Au collisions at √sNN from 20 to 7 GeV in collider mode, and from √sNN 7 to 3.5 GeV in the fixed target mode, which will be run concurrently with the collider mode operation.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes.

PubMed

Mougiakakou, Stavroula G; Prountzou, Aikaterini; Iliopoulou, Dimitra; Nikita, Konstantina S; Vazeou, Andriani; Bartsocas, Christos S

2006-01-01

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Future of the Beam Energy Scan program at RHIC

DOE PAGES

Odyniec, Grazyna; Bravina, L.; Foka, Y.; ...

2015-05-29

The first exploratory phase of a very successful Beam Energy Scan Program at RHIC was completed in 2014 with Au+Au collisions at energies ranging from 7 to 39 GeV. Data sets taken earlier extended the upper limit of energy range to the √sNN of 200 GeV. This provided an initial look into the uncharted territory of the QCD phase diagram, which is considered to be the single most important graph of our field. The main results from BES phase I, although effected by large statistical errors (steeply increasing with decreasing energy), suggest that the highest potential for discovery of themore » QCD Critical Point lies bellow √sNN 20 GeV. Here, we discuss the plans and the preparation for phase II of the BES program, with an order of magnitude larger statistics, which is planned for 2018-2019. The BES II will focus on Au+Au collisions at √sNN from 20 to 7 GeV in collider mode, and from √sNN 7 to 3.5 GeV in the fixed target mode, which will be run concurrently with the collider mode operation.« less

Dilepton Production in p+p, Au+Au collisions at √{sNN} = 200GeV and U+U collisions at √{sNN} = 193GeV

NASA Astrophysics Data System (ADS)

Brandenburg, James D.; STAR Collaboration

2017-11-01

In this contribution we report e+e1 spectra with various invariant mass and pT differentials in Au+Au collisions at √{sNN} = 200GeV and U+U collisions at √{sNN} = 193GeV. The structure of the t (- t ≈ pT2) distributions of these mass regions will be shown and compared with the same distributions in ultra-peripheral collisions. Additionally, this contribution discusses first measurements of μ+μ- invariant mass spectra from STAR's recently installed Muon Telescope Detector (MTD) in p+p and Au+Au collisions at √{sNN} = 200GeV.

Effects of climate change on Salmonella infections.

PubMed

Akil, Luma; Ahmad, H Anwar; Reddy, Remata S

2014-12-01

Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used. Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN. A seasonal trend in Salmonella infections was observed (p<0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R(2)=0.554; R(2)=0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed. There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections.

High-Accuracy Methods for Numerical Flow Analysis Using Adaptive Non-Linear Wavelets

DTIC Science & Technology

2012-08-01

n ji n ji  , )(~ ,, Opp n jin ji  , (2-8) )(~ 2/12/1  OEE nn  , )(~ 2/12/1 OFF nn  . Also, if the flux values are discretized with...jin ji  , (3-5) )(~ 2/12/1  OEE nn  , )(~ 2/12/1 OFF nn  . Also, if the flux values are discretized with the lth order of spatial

Transverse energy production and charged-particle multiplicity at midrapidity in various systems from √{sN N}=7.7 to 200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Afanasiev, S.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Al-Bataineh, H.; Alexander, J.; Alfred, M.; Al-Jamel, A.; Al-Ta'Ani, H.; Angerami, A.; Aoki, K.; Apadula, N.; Aphecetche, L.; Aramaki, Y.; Armendariz, R.; Aronson, S. H.; Asai, J.; Asano, H.; Aschenauer, E. C.; Atomssa, E. T.; Averbeck, R.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Bai, X.; Baksay, G.; Baksay, L.; Baldisseri, A.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Barnes, P. D.; Bassalleck, B.; Basye, A. T.; Bathe, S.; Batsouli, S.; Baublis, V.; Bauer, F.; Baumann, C.; Baumgart, S.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belikov, S.; Belmont, R.; Bennett, R.; Berdnikov, A.; Berdnikov, Y.; Bhom, J. H.; Bickley, A. A.; Bjorndal, M. T.; Black, D.; Blau, D. S.; Boissevain, J. G.; Bok, J. S.; Borel, H.; Boyle, K.; Brooks, M. L.; Brown, D. S.; Bryslawskyj, J.; Bucher, D.; Buesching, H.; Bumazhnov, V.; Bunce, G.; Burward-Hoy, J. M.; Butsyk, S.; Campbell, S.; Caringi, A.; Castera, P.; Chai, J.-S.; Chang, B. S.; Charvet, J.-L.; Chen, C.-H.; Chernichenko, S.; Chi, C. Y.; Chiba, J.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choi, S.; Choudhury, R. K.; Christiansen, P.; Chujo, T.; Chung, P.; Churyn, A.; Chvala, O.; Cianciolo, V.; Citron, Z.; Cleven, C. R.; Cobigo, Y.; Cole, B. A.; Comets, M. P.; Conesa Del Valle, Z.; Connors, M.; Constantin, P.; Cronin, N.; Crossette, N.; Csanád, M.; Csörgő, T.; Dahms, T.; Dairaku, S.; Danchev, I.; Danley, T. W.; Das, K.; Datta, A.; Daugherity, M. S.; David, G.; Dayananda, M. K.; Deaton, M. B.; Deblasio, K.; Dehmelt, K.; Delagrange, H.; Denisov, A.; D'Enterria, D.; Deshpande, A.; Desmond, E. J.; Dharmawardane, K. V.; Dietzsch, O.; Ding, L.; Dion, A.; Diss, P. B.; Do, J. H.; Donadelli, M.; D'Orazio, L.; Drachenberg, J. L.; Drapier, O.; Drees, A.; Drees, K. A.; Dubey, A. K.; Durham, J. M.; Durum, A.; Dutta, D.; Dzhordzhadze, V.; Edwards, S.; Efremenko, Y. V.; Egdemir, J.; Ellinghaus, F.; Emam, W. S.; Engelmore, T.; Enokizono, A.; En'yo, H.; Espagnon, B.; Esumi, S.; Eyser, K. O.; Fadem, B.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Forestier, B.; Fraenkel, Z.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fujiwara, K.; Fukao, Y.; Fung, S.-Y.; Fusayasu, T.; Gadrat, S.; Gainey, K.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, A.; Garishvili, I.; Gastineau, F.; Ge, H.; Germain, M.; Giordano, F.; Glenn, A.; Gong, H.; Gong, X.; Gonin, M.; Gosset, J.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grim, G.; Grosse Perdekamp, M.; Gu, Y.; Gunji, T.; Guo, L.; Guragain, H.; Gustafsson, H.-Å.; Hachiya, T.; Hadj Henni, A.; Haegemann, C.; Haggerty, J. S.; Hagiwara, M. N.; Hahn, K. I.; Hamagaki, H.; Hamblen, J.; Hamilton, H. F.; Han, R.; Han, S. Y.; Hanks, J.; Harada, H.; Hartouni, E. P.; Haruna, K.; Harvey, M.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; Haslum, E.; Hasuko, K.; Hayano, R.; Hayashi, S.; He, X.; Heffner, M.; Hemmick, T. K.; Hester, T.; Heuser, J. M.; Hiejima, H.; Hill, J. C.; Hobbs, R.; Hohlmann, M.; Hollis, R. S.; Holmes, M.; Holzmann, W.; Homma, K.; Hong, B.; Horaguchi, T.; Hori, Y.; Hornback, D.; Hoshino, T.; Hotvedt, N.; Huang, J.; Huang, S.; Hur, M. G.; Ichihara, T.; Ichimiya, R.; Iinuma, H.; Ikeda, Y.; Imai, K.; Imazu, Y.; Imrek, J.; Inaba, M.; Inoue, Y.; Iordanova, A.; Isenhower, D.; Isenhower, L.; Ishihara, M.; Isinhue, A.; Isobe, T.; Issah, M.; Isupov, A.; Ivanishchev, D.; Iwanaga, Y.; Jacak, B. V.; Javani, M.; Jeon, S. J.; Jezghani, M.; Jia, J.; Jiang, X.; Jin, J.; Jinnouchi, O.; Johnson, B. M.; Jones, T.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kajihara, F.; Kametani, S.; Kamihara, N.; Kamin, J.; Kanda, S.; Kaneta, M.; Kaneti, S.; Kang, B. H.; Kang, J. H.; Kang, J. S.; Kanou, H.; Kapustinsky, J.; Karatsu, K.; Kasai, M.; Kawagishi, T.; Kawall, D.; Kawashima, M.; Kazantsev, A. V.; Kelly, S.; Kempel, T.; Key, J. A.; Khachatryan, V.; Khandai, P. K.; Khanzadeev, A.; Kijima, K. M.; Kikuchi, J.; Kim, A.; Kim, B. I.; Kim, C.; Kim, D. H.; Kim, D. J.; Kim, E.; Kim, E.-J.; Kim, G. W.; Kim, H. J.; Kim, K.-B.; Kim, M.; Kim, Y.-J.; Kim, Y. K.; Kim, Y.-S.; Kimelman, B.; Kinney, E.; Kiss, Á.; Kistenev, E.; Kitamura, R.; Kiyomichi, A.; Klatsky, J.; Klay, J.; Klein-Boesing, C.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kochenda, L.; Kochetkov, V.; Kofarago, M.; Komatsu, Y.; Komkov, B.; Konno, M.; Koster, J.; Kotchetkov, D.; Kotov, D.; Kozlov, A.; Král, A.; Kravitz, A.; Krizek, F.; Kroon, P. J.; Kubart, J.; Kunde, G. J.; Kurihara, N.; Kurita, K.; Kurosawa, M.; Kweon, M. J.; Kwon, Y.; Kyle, G. S.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Le Bornec, Y.; Leckey, S.; Lee, B.; Lee, D. M.; Lee, G. H.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, M. K.; Lee, S.; Lee, S. H.; Lee, S. R.; Lee, T.; Leitch, M. J.; Leite, M. A. L.; Leitgab, M.; Lenzi, B.; Lewis, B.; Li, X.; Li, X. H.; Lichtenwalner, P.; Liebing, P.; Lim, H.; Lim, S. H.; Linden Levy, L. A.; Liška, T.; Litvinenko, A.; Liu, H.; Liu, M. X.; Love, B.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Makek, M.; Malakhov, A.; Malik, M. D.; Manion, A.; Manko, V. I.; Mannel, E.; Mao, Y.; Maruyama, T.; Mašek, L.; Masui, H.; Masumoto, S.; Matathias, F.; McCain, M. C.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Means, N.; Meles, A.; Mendoza, M.; Meredith, B.; Miake, Y.; Mibe, T.; Midori, J.; Mignerey, A. C.; Mikeš, P.; Miki, K.; Miller, T. E.; Milov, A.; Mioduszewski, S.; Mishra, D. K.; Mishra, G. C.; Mishra, M.; Mitchell, J. T.; Mitrovski, M.; Miyachi, Y.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Mohapatra, S.; Montuenga, P.; Moon, H. J.; Moon, T.; Morino, Y.; Morreale, A.; Morrison, D. P.; Moskowitz, M.; Moss, J. M.; Motschwiller, S.; Moukhanova, T. V.; Mukhopadhyay, D.; Murakami, T.; Murata, J.; Mwai, A.; Nagae, T.; Nagamiya, S.; Nagashima, K.; Nagata, Y.; Nagle, J. L.; Naglis, M.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nam, S.; Nattrass, C.; Nederlof, A.; Netrakanti, P. K.; Newby, J.; Nguyen, M.; Nihashi, M.; Niida, T.; Nishimura, S.; Norman, B. E.; Nouicer, R.; Novák, T.; Novitzky, N.; Nukariya, A.; Nyanin, A. S.; Nystrand, J.; Oakley, C.; Obayashi, H.; O'Brien, E.; Oda, S. X.; Ogilvie, C. A.; Ohnishi, H.; Oide, H.; Ojha, I. D.; Oka, M.; Okada, K.; Omiwade, O. O.; Onuki, Y.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Otterlund, I.; Ouchida, M.; Ozawa, K.; Pak, R.; Pal, D.; Palounek, A. P. T.; Pantuev, V.; Papavassiliou, V.; Park, B. H.; Park, I. H.; Park, J.; Park, J. S.; Park, S.; Park, S. K.; Park, W. J.; Pate, S. F.; Patel, L.; Patel, M.; Pei, H.; Peng, J.-C.; Pereira, H.; Perepelitsa, D. V.; Perera, G. D. N.; Peresedov, V.; Peressounko, D. Yu.; Perry, J.; Petti, R.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Proissl, M.; Purschke, M. L.; Purwar, A. K.; Qu, H.; Rak, J.; Rakotozafindrabe, A.; Ramson, B. J.; Ravinovich, I.; Read, K. F.; Rembeczki, S.; Reuter, M.; Reygers, K.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Rinn, T.; Riveli, N.; Roach, D.; Roche, G.; Rolnick, S. D.; Romana, A.; Rosati, M.; Rosen, C. A.; Rosendahl, S. S. E.; Rosnet, P.; Rowan, Z.; Rubin, J. G.; Rukoyatkin, P.; Ružička, P.; Rykov, V. L.; Ryu, M. S.; Ryu, S. S.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sakai, S.; Sakashita, K.; Sakata, H.; Sako, H.; Samsonov, V.; Sano, M.; Sano, S.; Sarsour, M.; Sato, H. D.; Sato, S.; Sato, T.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Sekiguchi, Y.; Semenov, V.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shaver, A.; Shea, T. K.; Shein, I.; Shevel, A.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shohjoh, T.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Silvestre, C.; Sim, K. S.; Singh, B. K.; Singh, C. P.; Singh, V.; Skolnik, M.; Skutnik, S.; Slunečka, M.; Smith, W. C.; Snowball, M.; Solano, S.; Soldatov, A.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Staley, F.; Stankus, P. W.; Steinberg, P.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Stone, M. R.; Sugitate, T.; Suire, C.; Sukhanov, A.; Sullivan, J. P.; Sumita, T.; Sun, J.; Sziklai, J.; Tabaru, T.; Takagi, S.; Takagui, E. M.; Takahara, A.; Taketani, A.; Tanabe, R.; Tanaka, K. H.; Tanaka, Y.; Taneja, S.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tarján, P.; Tennant, E.; Themann, H.; Thomas, D.; Thomas, T. L.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Togawa, M.; Toia, A.; Tojo, J.; Tomášek, L.; Tomášek, M.; Torii, H.; Towell, C. L.; Towell, R.; Towell, R. S.; Tram, V.-N.; Tserruya, I.; Tsuchimoto, Y.; Tsuji, T.; Tuli, S. K.; Tydesjö, H.; Tyurin, N.; Vale, C.; Valle, H.; van Hecke, H. W.; Vargyas, M.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Vinogradov, A. A.; Virius, M.; Voas, B.; Vossen, A.; Vrba, V.; Vznuzdaev, E.; Wagner, M.; Walker, D.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Wei, R.; Wessels, J.; Whitaker, S.; White, A. S.; White, S. N.; Willis, N.; Winter, D.; Wolin, S.; Woody, C. L.; Wright, R. M.; Wysocki, M.; Xia, B.; Xie, W.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yamaura, K.; Yang, R.; Yanovich, A.; Yasin, Z.; Ying, J.; Yokkaichi, S.; Yoo, J. H.; Yoon, I.; You, Z.; Young, G. R.; Younus, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zaudtke, O.; Zelenski, A.; Zhang, C.; Zhou, S.; Zimamyi, J.; Zolin, L.; Zou, L.; Phenix Collaboration

2016-02-01

Measurements of midrapidity charged-particle multiplicity distributions, d Nch/d η , and midrapidity transverse-energy distributions, d ET/d η , are presented for a variety of collision systems and energies. Included are distributions for Au +Au collisions at √{sNN}=200 , 130, 62.4, 39, 27, 19.6, 14.5, and 7.7 GeV, Cu +Cu collisions at √{sNN}=200 and 62.4 GeV, Cu +Au collisions at √{sNN}=200 GeV, U +U collisions at √{sNN}=193 GeV, d +Au collisions at √{sNN}=200 GeV, 3He+Au collisions at √{sNN}=200 GeV, and p +p collisions at √{sNN}=200 GeV. Centrality-dependent distributions at midrapidity are presented in terms of the number of nucleon participants, Npart, and the number of constituent quark participants, Nqp. For all A +A collisions down to √{sNN}=7.7 GeV, it is observed that the midrapidity data are better described by scaling with Nqp than scaling with Npart. Also presented are estimates of the Bjorken energy density, ɛBJ, and the ratio of d ET/d η to d Nch/d η , the latter of which is seen to be constant as a function of centrality for all systems.

Alpha List of Prime Contract Awards. Oct 91 - Sep 92. FY 92. (Reichlmeter - Season All Industries Inc)

DTIC Science & Technology

1993-01-01

11 0 1 b.4- 1 CL- 0 0 - V0- ~ - . - -II 0w- 0 0 0 0 0 0 0 Ii ’ 1 I O C 4 0 0 (D a0 - ( N 1,4 40 4 4 4 4 4 0 4 4 0 4 4 4coo0 400 it 1M (7 4 0 0 Ip -j...C.Q00000 0C0 k0 a010 00 000 C) N5 N 1-if Nf N IP -NN N NN 14P4NN NN N NN N N NI N NN N N NN’N NJ10Q-t- if 7 ,2ZZZZ ZZZZZZZZZZZZZZ2ZZZ ZZ zzz 4w 140000...0 0< 4 0ɜ 04 0ɜɜ 04=(0C R 2 2 Z z 22 Zzz 2 2 222 z PP P00-w P4 N 0 44 0 4 10wCo0mm < ca 0 iP 0 0 0 - P 0 0c c % U -4mi0 N m0 PIS 0-400 N 0

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.

PubMed

Krasnopolsky, Vladimir; Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

2016-01-01

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed--signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

PubMed Central

Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

2016-01-01

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series. PMID:26819586

Correlational Effects of the Molecular-Tilt Configuration and the Intermolecular van der Waals Interaction on the Charge Transport in the Molecular Junction.

PubMed

Shin, Jaeho; Gu, Kyungyeol; Yang, Seunghoon; Lee, Chul-Ho; Lee, Takhee; Jang, Yun Hee; Wang, Gunuk

2018-06-25

Molecular conformation, intermolecular interaction, and electrode-molecule contacts greatly affect charge transport in molecular junctions and interfacial properties of organic devices by controlling the molecular orbital alignment. Here, we statistically investigated the charge transport in molecular junctions containing self-assembled oligophenylene molecules sandwiched between an Au probe tip and graphene according to various tip-loading forces ( F L ) that can control the molecular-tilt configuration and the van der Waals (vdW) interactions. In particular, the molecular junctions exhibited two distinct transport regimes according to the F L dependence (i.e., F L -dependent and F L -independent tunneling regimes). In addition, the charge-injection tunneling barriers at the junction interfaces are differently changed when the F L ≤ 20 nN. These features are associated to the correlation effects between the asymmetry-coupling factor (η), the molecular-tilt angle (θ), and the repulsive intermolecular vdW force ( F vdW ) on the molecular-tunneling barriers. A more-comprehensive understanding of these charge transport properties was thoroughly developed based on the density functional theory calculations in consideration of the molecular-tilt configuration and the repulsive vdW force between molecules.

Observation of charge asymmetry dependence of pion elliptic flow and the possible chiral magnetic wave in heavy-ion collisions

DOE PAGES

Adamczyk, L.

2015-06-26

We present measurements of π⁻ and π⁺ elliptic flow, v₂, at midrapidity in Au+Au collisions at √s NN = 200, 62.4, 39, 27, 19.6, 11.5, and 7.7 GeV, as a function of event-by-event charge asymmetry, A ch, based on data from the STAR experiment at RHIC. We find that π⁻ (π⁺) elliptic flow linearly increases (decreases) with charge asymmetry for most centrality bins at √s NN = 27 GeV and higher. At √s NN = 200 GeV, the slope of the difference of v₂ between π⁻ and π⁺ as a function of A ch exhibits a centrality dependence, which ismore » qualitatively similar to calculations that incorporate a chiral magnetic wave effect. In addition, similar centrality dependence is also observed at lower energies.« less

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Zero-sum two-player game theoretic formulation of affine nonlinear discrete-time systems using neural networks.

PubMed

Mehraeen, Shahab; Dierks, Travis; Jagannathan, S; Crow, Mariesa L

2013-12-01

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.

Elliptic flow of charm and strange hadrons in high-multiplicity pPb collisions at $$\\sqrt{s_{_\\mathrm{NN}}} =$$ 8.16 TeV

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

Sirunyan, Albert M; et al.

The elliptic azimuthal anisotropy coefficient (more » $$v_2$$) is measured for charm (D$^0$) and strange (K$$_\\mathrm{S}^0$$, $$\\Lambda$$, $$\\Xi^-$$, and $$\\Omega^-$$) hadrons, using a data sample of pPb collisions collected by the CMS experiment, at a nucleon-nucleon center-of-mass energy $$\\sqrt{s_{_\\mathrm{NN}}} =$$ 8.16 TeV. A significant positive $$v_2$$ signal from long-range azimuthal correlations is observed for all particle species in high-multiplicity pPb collisions. The measurement represents the first observation of possible long-range collectivity for open heavy flavor hadrons in small systems. The results suggest that charm quarks have a smaller $$v_2$$ than the lighter quarks, probably reflecting a weaker collective behavior. This effect is not seen in the larger PbPb collision system at $$\\sqrt{s_{_\\mathrm{NN}}} =$$ 5.02 TeV, also presented.« less

Neural networks for Higgs physics

NASA Astrophysics Data System (ADS)

Tentindo-Repond, Silvia; Bhat, Pushpalatha C.; Prosper, Harrison B.

2001-08-01

The main application of neural networks (NN) in Higgs physics so far has been to optimize the signal over background ratio. The positive result obtained imply that the use of NN will lead to a big reduction in the integrated luminosity required for the discovery of the Higgs in RunII. Neural Networks have also been recently used in Higgs physics to set up tagging algorithms to identify the heavy flavor content of jets. Whereas in the previous studies the NN b-tagging methods used are channel-independent, a channel-dependent method has been used in the present work. The signal pp¯→WH→lνbb¯ has been studied against the dominant background pp¯→Wbb¯, in an attempt to improve the signal over background ratio by trying to push the invariant mass of the background events further away from the signal. This result would get the equivalent effect of an improved mass resolution.

Phase behavior of Langmuir monolayers with ionic molecular heads: Molecular simulations

NASA Astrophysics Data System (ADS)

González-Castro, Carlos A.; Ramírez-Santiago, Guillermo

2015-03-01

We carried out Monte Carlo simulations in the N ,Π,T ensemble of a Langmuir monolayer coarse-grained molecular model. Considering that the hydrophilic groups can be ionized by modulating acid-base interactions, here we study the phase behavior of a model that incorporates the short-range steric and long-range ionic interactions. The simulations were carried out in the reduced temperature range 0.1 ≤T*<4.0 , where there is a competition of these interactions. Different order parameters were calculated and analyzed for several values of the reduced surface pressure in the interval, 1 ≤Π*≤40. For most of the surface pressures two directions of molecular tilt were found: (i) towards the nearest neighbor (NN) at low temperatures, T*<0.7, and most of the values of Π* and (ii) towards next-nearest neighbors (NNN) in the temperature interval 0.7 ≤T*<1.1 for Π*<25. We also found the coexistence of the NN and NNN at intermediate temperatures and Π*>25 . A low-temperature reentrant disorder-order-disorder transition in the positions of the molecular heads and in the collective tilt of the tails was found for all the surface pressure values. It was also found that the molecular tails arranged forming "rotating patterns" in the temperature interval, 0.5

Reinforcement-learning-based dual-control methodology for complex nonlinear discrete-time systems with application to spark engine EGR operation.

PubMed

Shih, Peter; Kaul, Brian C; Jagannathan, S; Drallmeier, James A

2008-08-01

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach.

Proteomic Investigation to Identify Anticancer Targets of Nemopilema nomurai Jellyfish Venom in Human Hepatocarcinoma HepG2 Cells

PubMed Central

Choudhary, Indu; Lee, Hyunkyoung; Pyo, Min Jung; Heo, Yunwi; Chae, Jinho; Yum, Seung Shic; Kang, Changkeun; Kim, Euikyung

2018-01-01

Nemopilema nomurai is a giant jellyfish that blooms in East Asian seas. Recently, N. nomurai venom (NnV) was characterized from a toxicological and pharmacological point of view. A mild dose of NnV inhibits the growth of various kinds of cancer cells, mainly hepatic cancer cells. The present study aims to identify the potential therapeutic targets and mechanism of NnV in the growth inhibition of cancer cells. Human hepatocellular carcinoma (HepG2) cells were treated with NnV, and its proteome was analyzed using two-dimensional gel electrophoresis, followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI/TOF/MS). The quantity of twenty four proteins in NnV-treated HepG2 cells varied compared to non-treated control cells. Among them, the amounts of fourteen proteins decreased and ten proteins showed elevated levels. We also found that the amounts of several cancer biomarkers and oncoproteins, which usually increase in various types of cancer cells, decreased after NnV treatment. The representative proteins included proliferating cell nuclear antigen (PCNA), glucose-regulated protein 78 (GRP78), glucose-6-phosphate dehydrogenase (G6PD), elongation factor 1γ (EF1γ), nucleolar and spindle-associated protein (NuSAP), and activator of 90 kDa heat shock protein ATPase homolog 1 (AHSA1). Western blotting also confirmed altered levels of PCNA, GRP78, and G6PD in NnV-treated HepG2 cells. In summary, the proteomic approach explains the mode of action of NnV as an anticancer agent. Further characterization of NnV may help to unveil novel therapeutic agents in cancer treatment. PMID:29748501

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

PubMed

Khondoker, Mizanur; Dobson, Richard; Skirrow, Caroline; Simmons, Andrew; Stahl, Daniel

2016-10-01

Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study. © The Author(s) 2013.

Cardiac autonomic profile in different sports disciplines during all-day activity.

PubMed

Sztajzel, J; Jung, M; Sievert, K; Bayes De Luna, A

2008-12-01

Physical training and sport activity have a beneficial effect on cardiac autonomic activity. However, the exact impact of different types of sports disciplines on cardiac autonomic function is still unclear. The aim of this study was to evaluate the cardiac autonomic profile in different sports discplines and to determine their impact on cardiac autonomic function by using heart rate variability (HRV), a noninvasive electrocardiographic (ECG) analysis of the sympatho-vagal balance. Temporal and spectral HRV parameters determined from 24-hour continuous ECG monitoring were studied in 40 subjects, including 12 endurance athletes, 14 hockey players and 14 untrained male volunteers (control group). Each participant had to wear a Holter recorder during 24 hours and to continue his everyday activities. All HRV parameters were compared between the 3 study groups. All heart rate values were lower and all parasympathetic-related time domain indices, including root mean square of successive differences (RMSSD) and pNN50 (NN50 count divided by the total number of all NN intervals), were higher in both athletes groups as compared with controls (P<0.05). However, standard deviation of all NN intervals (SDNN) values, which determine global HRV, were significantly higher only in endurance athletes (P<0.05). Furthermore, the power spectral components low frequency (LF), a mixture of both autonomic inputs, and HF (high frequency), a marker of vagal modulation, were significantly higher with a resulting lower LF/HF ratio in both athletes groups as compared to controls (P<0.05). Both endurance and team playing athletic activity induce during all-day a high parasympathetic tone (higher RMSSD, pNN50 and HF, and lower LF/HF ratio). However, only endurance athletic activity has a particularly high global HRV (higher SDNN), indicating thereby that this type sports discipline may have a more substantially favorable effect on the cardiac autonomic profile.

Production of e+e- from U+U collisions at √{sNN } = 193 GeV and Au+Au collisions at √{sNN } = 19.6 , 27 , 39 , 62.4, and 200 GeV as measured by STAR

NASA Astrophysics Data System (ADS)

Butterworth, Joey; STAR Collaboration

2017-08-01

We present STAR's measurement of the e+e- continuum as a function of centrality, invariant mass, and transverse momentum for U+U collisions at √{sNN } = 193 GeV. Also reported are the acceptance-corrected e+e- invariant mass spectra for minimum-bias Au+Au collisions at √{sNN } = 27 , 39, and 62.4 GeV and U+U collisions at √{sNN } = 193 GeV. The connection between the integrated e+e- excess yields normalized by charge particle multiplicity (dNch / dy) at mid-rapidity and the lifetime of the fireball is discussed.

Effect of Förster-mediated triplet-polaron quenching and triplet-triplet annihilation on the efficiency roll-off of organic light-emitting diodes

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

Eersel, H. van; Bobbert, P. A.; Janssen, R. A. J.

2016-04-28

We report the results of a systematic study of the interplay of triplet-polaron quenching (TPQ) and triplet-triplet annihilation (TTA) on the efficiency roll-off of organic light-emitting diodes (OLEDs) with increasing current density. First, we focus on OLEDs based on the green phosphorescent emitter tris[2-phenylpyridine]iridium(III) (Ir(ppy){sub 3}) and the red phosphorescent dye platinum octaethylporphyrin. It is found that the experimental data can be reproduced using kinetic Monte Carlo (kMC) simulations within which TPQ and TTA are due to a nearest-neighbor (NN) interaction, or due to a more long-range Förster-type process. Furthermore, we find a subtle interplay between TPQ and TTA: decreasingmore » the contribution of one process can increase the contribution of the other process, so that the roll-off is not significantly reduced. Furthermore, we find that just analyzing the shape of the roll-off is insufficient for determining the relative role of TPQ and TTA. Subsequently, we investigate the wider validity of this picture using kMC simulations for idealized but realistic symmetric OLEDs, with an emissive layer containing a small concentration of phosphorescent dye molecules in a matrix material. Whereas for NN-interactions the roll-off can be reduced when the dye molecules act as shallow hole and electron traps, we find that such an approach becomes counterproductive for long-range TTA and TPQ. Developing well-founded OLED design rules will thus require that more quantitative information is available on the rate and detailed mechanism of the TPQ and TTA processes.« less

New Amphiphilic Neamine Derivatives Active against Resistant Pseudomonas aeruginosa and Their Interactions with Lipopolysaccharides

PubMed Central

Sautrey, Guillaume; Zimmermann, Louis; Deleu, Magali; Delbar, Alicia; Souza Machado, Luiza; Jeannot, Katy; Van Bambeke, Françoise; Buyck, Julien M.; Decout, Jean-Luc

2014-01-01

The development of novel antimicrobial agents is urgently required to curb the widespread emergence of multidrug-resistant bacteria like colistin-resistant Pseudomonas aeruginosa. We previously synthesized a series of amphiphilic neamine derivatives active against bacterial membranes, among which 3′,6-di-O-[(2″-naphthyl)propyl]neamine (3′,6-di2NP), 3′,6-di-O-[(2″-naphthyl)butyl]neamine (3′,6-di2NB), and 3′,6-di-O-nonylneamine (3′,6-diNn) showed high levels of activity and low levels of cytotoxicity (L. Zimmermann et al., J. Med. Chem. 56:7691–7705, 2013). We have now further characterized the activity of these derivatives against colistin-resistant P. aeruginosa and studied their mode of action; specifically, we characterized their ability to interact with lipopolysaccharide (LPS) and to alter the bacterial outer membrane (OM). The three amphiphilic neamine derivatives were active against clinical colistin-resistant strains (MICs, about 2 to 8 μg/ml), The most active one (3′,6-diNn) was bactericidal at its MIC and inhibited biofilm formation at 2-fold its MIC. They cooperatively bound to LPSs, increasing the outer membrane permeability. Grafting long and linear alkyl chains (nonyl) optimized binding to LPS and outer membrane permeabilization. The effects of amphiphilic neamine derivatives on LPS micelles suggest changes in the cross-bridging of lipopolysaccharides and disordering in the hydrophobic core of the micelles. The molecular shape of the 3′,6-dialkyl neamine derivatives induced by the nature of the grafted hydrophobic moieties (naphthylalkyl instead of alkyl) and the flexibility of the hydrophobic moiety are critical for their fluidifying effect and their ability to displace cations bridging LPS. Results from this work could be exploited for the development of new amphiphilic neamine derivatives active against colistin-resistant P. aeruginosa. PMID:24867965

Baryon-baryon interactions and spin-flavor symmetry from lattice quantum chromodynamics

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

Wagman, Michael L.; Winter, Frank; Chang, Emmanuel

Lattice quantum chromodynamics is used to constrain the interactions of two octet baryons at the SU(3) flavor-symmetric point, with quark masses that are heavier than those in nature (equal to that of the physical strange quark mass and corresponding to a pion mass ofmore » $$\\approx 806~\\tt{MeV}$$). Specifically, the S-wave scattering phase shifts of two-baryon systems at low energies are obtained with the application of L\\"uscher's formalism, mapping the energy eigenvalues of two interacting baryons in a finite volume to the two-particle scattering amplitudes below the relevant inelastic thresholds. The values of the leading-order low-energy scattering parameters in the irreducible representations of SU(3) are consistent with an approximate SU(6) spin-flavor symmetry in the nuclear and hypernuclear forces that is predicted in the large-$$N_c$$ limit of QCD. The two distinct SU(6)-invariant interactions between two baryons are constrained at this value of the quark masses, and their values indicate an approximate accidental SU(16) symmetry. The SU(3) irreducible representations containing the $$NN~({^1}S_0)$$, $$NN~({^3}S_1)$$ and $$\\frac{1}{\\sqrt{2}}(\\Xi^0n+\\Xi^-p)~({^3}S_1)$$ channels unambiguously exhibit a single bound state, while the irreducible representation containing the $$\\Sigma^+ p~({^3}S_1)$$ channel exhibits a state that is consistent with either a bound state or a scattering state close to threshold. These results are in agreement with the previous conclusions of the NPLQCD collaboration regarding the existence of two-nucleon bound states at this value of the quark masses.« less

Baryon-baryon interactions and spin-flavor symmetry from lattice quantum chromodynamics

DOE PAGES

Wagman, Michael L.; Winter, Frank; Chang, Emmanuel; ...

2017-12-28

Lattice quantum chromodynamics is used to constrain the interactions of two octet baryons at the SU(3) flavor-symmetric point, with quark masses that are heavier than those in nature (equal to that of the physical strange quark mass and corresponding to a pion mass ofmore » $$\\approx 806~\\tt{MeV}$$). Specifically, the S-wave scattering phase shifts of two-baryon systems at low energies are obtained with the application of L\\"uscher's formalism, mapping the energy eigenvalues of two interacting baryons in a finite volume to the two-particle scattering amplitudes below the relevant inelastic thresholds. The values of the leading-order low-energy scattering parameters in the irreducible representations of SU(3) are consistent with an approximate SU(6) spin-flavor symmetry in the nuclear and hypernuclear forces that is predicted in the large-$$N_c$$ limit of QCD. The two distinct SU(6)-invariant interactions between two baryons are constrained at this value of the quark masses, and their values indicate an approximate accidental SU(16) symmetry. The SU(3) irreducible representations containing the $$NN~({^1}S_0)$$, $$NN~({^3}S_1)$$ and $$\\frac{1}{\\sqrt{2}}(\\Xi^0n+\\Xi^-p)~({^3}S_1)$$ channels unambiguously exhibit a single bound state, while the irreducible representation containing the $$\\Sigma^+ p~({^3}S_1)$$ channel exhibits a state that is consistent with either a bound state or a scattering state close to threshold. These results are in agreement with the previous conclusions of the NPLQCD collaboration regarding the existence of two-nucleon bound states at this value of the quark masses.« less

Hard Break-Up of Two-Nucleons and QCD Dynamics of NN Interaction

NASA Astrophysics Data System (ADS)

Sargsian, Misak; Granados, Carlos

2009-05-01

We investigate hard photodisintegration of two nucleons from ^3He nucleus within the framework of hard rescattering model (HRM). In HRM a quark of one nucleon knocked-out by incoming photon rescatters with a quark of the other nucleon leading to the production of two nucleons with high relative momentum. HRM allows to express the amplitude of two-nucleon break-up reaction through the convolution of photon-quark scattering, NN hard scattering amplitude and nuclear spectral function which can be calculated using nonrelativistic ^3He wave function. HRM predicts several specific features for hard break-up reaction. First, the cross section will approximately scale as s-11. Also one predicts comparable or larger cross section for pp break up as compared to that of pn break-up, which is opposite to what is observed in low energy kinematics. Another result is the prediction of different spectator momentum dependencies of pp and pn break-up cross sections. This is due to the fact that same-helicity pp-component is strongly suppressed in the ground state wave function of ^3He. Due to this suppression HRM predicts significantly different asymmetries for the cross section of polarization transfer NN break-up reactions for circularly polarized photons. For the pp break-up this asymmetry is predicted to be zero while for the pn it is close to 23.

Handling limited datasets with neural networks in medical applications: A small-data approach.

PubMed

Shaikhina, Torgyn; Khovanova, Natalia A

2017-01-01

Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Comparison of Sleep Disorders between Real and Simulated 3,450-m Altitude.

PubMed

Heinzer, Raphaël; Saugy, Jonas J; Rupp, Thomas; Tobback, Nadia; Faiss, Raphael; Bourdillon, Nicolas; Rubio, José Haba; Millet, Grégoire P

2016-08-01

Hypoxia is known to generate sleep-disordered breathing but there is a debate about the pathophysiological responses to two different types of hypoxic exposure: normobaric hypoxia (NH) and hypobaric hypoxia (HH), which have never been directly compared. Our aim was to compare sleep disorders induced by these two types of altitude. Subjects were exposed to 26 h of simulated (NH) or real altitude (HH) corresponding to 3,450 m and a control condition (NN) in a randomized order. The sleep assessments were performed with nocturnal polysomnography (PSG) and questionnaires. Thirteen healthy trained males subjects volunteered for this study (mean ± SD; age 34 ± 9 y, body weight 76.2 ± 6.8 kg, height 179.7 ± 4.2 cm). Mean nocturnal oxygen saturation was further decreased during HH than in NH (81.2 ± 3.1 versus 83.6 ± 1.9%; P < 0.01) when compared to NN (95.5 ± 0.9%; P < 0.001). Heart rate was higher in HH than in NH (61 ± 10 versus 55 ± 6 bpm; P < 0.05) and NN (48 ± 5 bpm; P < 0.001). Total sleep time was longer in HH than in NH (351 ± 63 versus 317 ± 65 min, P < 0.05), and both were shorter compared to NN (388 ± 50 min, P < 0.05). Breathing frequency did not differ between conditions. Apnea-hypopnea index was higher in HH than in NH (20.5 [15.8-57.4] versus 11.4 [5.0-65.4]; P < 0.01) and NN (8.2 [3.9-8.8]; P < 0.001). Subjective sleep quality was similar between hypoxic conditions but lower than in NN. Our results suggest that HH has a greater effect on nocturnal breathing and sleep structure than NH. In HH, we observed more periodic breathing, which might arise from the lower saturation due to hypobaria, but needs to be confirmed. © 2016 Associated Professional Sleep Societies, LLC.

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Transverse energy production and charged-particle multiplicity at midrapidity in various systems from s N N = 7.7 to 200 GeV

DOE PAGES

Adare, A.; Afanasiev, S.; Aidala, C.; ...

2016-02-03

Measurements of midrapidity charged-particle multiplicity distributions, dN ch/dη, and midrapidity transverse-energy distributions, dE T/dη, are presented for a variety of collision systems and energies. Included are distributions for Au+Au collisions at √s NN=200, 130, 62.4, 39, 27, 19.6, 14.5, and 7.7 GeV, Cu+Cu collisions at √s NN=200 and 62.4 GeV, Cu+Au collisions at √s NN=200 GeV, U+U collisions at√s NN=193 GeV, d+Au collisions at √s NN=200 GeV, He3+Au collisions at √s NN=200 GeV, and p+p collisions at √s NN=200 GeV. We present centrality-dependent distributions at midrapidity in terms of the number of nucleon participants, N part, and the number ofmore » constituent quark participants, N qp. For all A+A collisions down to √s NN=7.7 GeV, we observed that the midrapidity data are better described by scaling with N qp than scaling with N part. Finally, our estimates of the Bjorken energy density, ε BJ, and the ratio of dE T/dη to dN ch/dη are presented, the latter of which is seen to be constant as a function of centrality for all systems.« less

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

NASA Astrophysics Data System (ADS)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

Collapse of the sea surface stability during the Miocene to Quartenary in the Western Pacific Ocean, indicated by Discoaster abundance and Coccolith size change

NASA Astrophysics Data System (ADS)

Sato, T.; Pratiwi, S. D.; Farida, M.

2013-12-01

We describe in detail the middle Miocene to Pleistocene paleoceanography of the Western Pacific Ocean based on calcareous nannofossils. Abundantly occurrence of discoasters, which indicates the stable sea surface stratification and the development of thermo- and nutri-cline, are found in the interval from NN2 to NN4 zones of the early Miocene. The relative abundance of discoaster is decreased in the NN4-5 zone and it changed to very rare above NN10 (B in Fig.1). These characteristics are found in both Sites 805 and 782. Focusing to the mean size of Reticulofenestra species, it decreased at NN4-5 zone (A in Fig 2), and lower part of NN11 (B in Fig. 2). The presence of larger size Reticulofenestra species also show the oligotrophic conditions of sea surface with thermocline. On the basis of these results, the collapse of the stability of the sea surface stratification in the Western Pacific Ocean progressed throughout the Miocene to Quaternary. As the results, nutrient conditions of sea surface in these area were changed in steps from oligotrophic to eutrophic conditions at NN4-5 and lower part of NN11 (A and B in Fig. 2). These datum related to collapse of sea surface conditions, is cleary correlated to the timing of the end of Mid-Miocene Climatic Optimum (A) and the intensify of the Asian Monsoon (B; Fig. 2).

Nonperturbative NN scattering in {sup 3}S{sub 1}–{sup 3}D{sub 1} channels of EFT(⁄π)

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

Yang, Ji-Feng, E-mail: jfyang@phy.ecnu.edu.cn

2013-12-15

The closed-form T matrices in the {sup 3}S{sub 1}–{sup 3}D{sub 1} channels of EFT(⁄π) for NN scattering with the potentials truncated at order O(Q{sup 4}) are presented with the nonperturbative divergences parametrized in a general manner. The stringent constraints imposed by the closed form of the T matrices are exploited in the underlying theory perspective and turned into virtues in the implementation of subtractions and the manifestation of power counting rules in nonperturbative regimes, leading us to the concept of EFT scenario. A number of scenarios of the EFT description of NN scattering are compared with PSA data in termsmore » of effective range expansion and {sup 3}S{sub 1} phase shifts, showing that it is favorable to proceed in a scenario with conventional EFT couplings and sophisticated renormalization in order to have large NN scattering lengths. The informative utilities of fine tuning are demonstrated in several examples and naturally interpreted in the underlying theory perspective. In addition, some of the approaches adopted in the recent literature are also addressed in the light of EFT scenario. -- Highlights: •Closed-form unitary T matrices for NN scattering are obtained in EFT(⁄π). •Nonperturbative properties inherent in such closed-form T matrices are explored. •Nonperturbative renormalization is implemented through exploiting these properties. •Unconventional power counting of couplings is shown to be less favored by PSA data. •The ideas about nonperturbative renormalization here might have wider applications.« less

Evaluation of Data Processing Techniques for Unobtrusive Gait Authentication

DTIC Science & Technology

2014-03-01

scatter plot depicting the performance of kNN , by TER, on all experimental mixtures...30  Table 9.  Mean TER of SVM and kNN performance with different voting parameters...performance on XYZ-axis data. ...........................................................51  Table 19.  kNN and SVM results in back pocket carrying

Energy dependence of acceptance-corrected dielectron excess mass spectrum at mid-rapidity in Au +Au collisions at √{sNN} = 19.6 and 200 GeV

NASA Astrophysics Data System (ADS)

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I.; Alford, J.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Averichev, G. S.; Banerjee, A.; Bellwied, R.; Bhasin, A.; Bhati, A. K.; Bhattarai, P.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Bouchet, J.; Brandin, A. V.; Bunzarov, I.; Burton, T. P.; Butterworth, J.; Caines, H.; Calder'on de la Barca S'anchez, M.; Campbell, J. M.; Cebra, D.; Cervantes, M. C.; Chakaberia, I.; Chaloupka, P.; Chang, Z.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Codrington, M. J. M.; Contin, G.; Crawford, H. J.; Das, S.; De Silva, L. C.; Debbe, R. R.; Dedovich, T. G.; Deng, J.; Derevschikov, A. A.; di Ruzza, B.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Draper, J. E.; Du, C. M.; Dunkelberger, L. E.; Dunlop, J. C.; Efimov, L. G.; Engelage, J.; Eppley, G.; Esha, R.; Evdokimov, O.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Fedorisin, J.; Feng; Filip, P.; Fisyak, Y.; Flores, C. E.; Fulek, L.; Gagliardi, C. A.; Garand, D.; Geurts, F.; Gibson, A.; Girard, M.; Greiner, L.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, S.; Gupta, A.; Guryn, W.; Hamad, A.; Hamed, A.; Haque, R.; Harris, J. W.; He, L.; Heppelmann, S.; Hirsch, A.; Hoffmann, G. W.; Hofman, D. J.; Horvat, S.; Huang, H. Z.; Huang, X.; Huang, B.; Huck, P.; Humanic, T. J.; Igo, G.; Jacobs, W. W.; Jang, H.; Jiang, K.; Judd, E. G.; Kabana, S.; Kalinkin, D.; Kang, K.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Khan, Z. H.; Kikola, D. P.; Kisel, I.; Kisiel, A.; Klein, S. R.; Koetke, D. D.; Kollegger, T.; Kosarzewski, L. K.; Kotchenda, L.; Kraishan, A. F.; Kravtsov, P.; Krueger, K.; Kulakov, I.; Kumar, L.; Kycia, R. A.; Lamont, M. A. C.; Landgraf, J. M.; Landry, K. D.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, X.; Li, X.; Li, W.; Li, Z. M.; Li, Y.; Li, C.; Lisa, M. A.; Liu, F.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, X.; Ma, L.; Ma, R.; Ma, G. L.; Ma, Y. G.; Magdy, N.; Majka, R.; Manion, A.; Margetis, S.; Markert, C.; Masui, H.; Matis, H. S.; McDonald, D.; Meehan, K.; Minaev, N. G.; Mioduszewski, S.; Mohanty, B.; Mondal, M. M.; Morozov, D. A.; Mustafa, M. K.; Nandi, B. K.; Nasim, Md.; Nayak, T. K.; Nigmatkulov, G.; Nogach, L. V.; Noh, S. Y.; Novak, J.; Nurushev, S. B.; Odyniec, G.; Ogawa, A.; Oh, K.; Okorokov, V.; Olvitt, D. L.; Page, B. S.; Pan, Y. X.; Pandit, Y.; Panebratsev, Y.; Pawlak, T.; Pawlik, B.; Pei, H.; Perkins, C.; Peterson, A.; Pile, P.; Planinic, M.; Pluta, J.; Poljak, N.; Poniatowska, K.; Porter, J.; Posik, M.; Poskanzer, A. M.; Pruthi, N. K.; Putschke, J.; Qiu, H.; Quintero, A.; Ramachandran, S.; Raniwala, R.; Raniwala, S.; Ray, R. L.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Roy, A.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Sakrejda, I.; Salur, S.; Sandacz, A.; Sandweiss, J.; Sarkar, A.; Schambach, J.; Scharenberg, R. P.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Seger, J.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Sharma, M. K.; Sharma, B.; Shen, W. Q.; Shi, S. S.; Shou, Q. Y.; Sichtermann, E. P.; Sikora, R.; Simko, M.; Skoby, M. J.; Smirnov, N.; Smirnov, D.; Solanki, D.; Song, L.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stock, R.; Strikhanov, M.; Stringfellow, B.; Sumbera, M.; Summa, B. J.; Sun, Y.; Sun, Z.; Sun, X. M.; Sun, X.; Surrow, B.; Svirida, D. N.; Szelezniak, M. A.; Takahashi, J.; Tang, A. H.; Tang, Z.; Tarnowsky, T.; Tawfik, A. N.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Tokarev, M.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Trzeciak, B. A.; Tsai, O. D.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; van Nieuwenhuizen, G.; Vandenbroucke, M.; Varma, R.; Vasiliev, A. N.; Vertesi, R.; Videbaek, F.; Viyogi, Y. P.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, Y.; Wang, F.; Wang, H.; Wang, J. S.; Wang, G.; Wang, Y.; Webb, J. C.; Webb, G.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y. F.; Xiao, Z.; Xie, W.; Xin, K.; Xu, Z.; Xu, Q. H.; Xu, N.; Xu, H.; Xu, Y. F.; Yang, Y.; Yang, C.; Yang, S.; Yang, Q.; Yang, Y.; Ye, Z.; Yepes, P.; Yi, L.; Yip, K.; Yoo, I.-K.; Yu, N.; Zbroszczyk, H.; Zha, W.; Zhang, J. B.; Zhang, X. P.; Zhang, S.; Zhang, J.; Zhang, Z.; Zhang, Y.; Zhang, J. L.; Zhao, F.; Zhao, J.; Zhong, C.; Zhou, L.; Zhu, X.; Zoulkarneeva, Y.; Zyzak, M.

2015-11-01

The acceptance-corrected dielectron excess mass spectra, where the known hadronic sources have been subtracted from the inclusive dielectron mass spectra, are reported for the first time at mid-rapidity |yee | < 1 in minimum-bias Au +Au collisions at √{sNN} = 19.6 and 200 GeV. The excess mass spectra are consistently described by a model calculation with a broadened ρ spectral function for Mee < 1.1 GeV /c2. The integrated dielectron excess yield at √{sNN} = 19.6 GeV for 0.4

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

PubMed

Biswas, Surama; Dutta, Subarna; Acharyya, Sriyankar

2017-12-01

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

Octahedral d[sup 6] Bis(maleimide) and Bis(maleic anhydride) complexes of molybdenum

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

Lai, Chen-Hsing; Cheng, Chien-Hong; Liao, Fen-Ling

1993-12-08

Mo(CO)[sub 3](CH[sub 3]CN)[sub 3] reacts with 2 equiv of alkene, where the alkene is maleimide (MI), N-phenylmaleimide (PhMI), or N-methylmaleimide, to give the corresponding Mo(CO)[sub 2](alkene)[sub 2](CH[sub 3]CN)[sub 2] complex (1a-c, respectively) in excellent yield. Dissolution of 1 in DMSO led to the substitution of acetonitrile ligands by DMSO to form the corresponding cis bis(DMSO) complexes 2a-c. Addition of 1 equiv of NN to 1 yields MO(CO)[sub 2](alkene)[sub 2](alkene)[sub 2](NN) (NN = en, alkene = PhMI (3b), MeMI (3c); NN = o-phenylenediamine, alkene = PhMI (4)). Treatment of Mo-(CO)[sub 4](NN) (NN = phen or bpy), with 2 equiv of alkenemore » in refluxed acetonitrile for 2 h gave Mo(CO)[sub 2]-(alkene)[sub 2](NN) (NN = phen, alkene = MI (5a), PhMI (5b); NN = bpy, alkene = MI (6a), PhMI (6b)). Treatment of Mo(CO)[sub 3](CH[sub 3]CN)[sub 3] with 2 equiv of maleic anhydride (MA) gave Mo(CO)[sub 2](MA)[sub 2](CH[sub 3]CN)[sub 2] (7). The acetonitrile ligands in 7 were replaced by DMSO molecules to give complex 8 as 7 was dissolved in DMSO. Similarly, the reaction of 7 with a bidentate ligand NN (phen or bpy) gave the substituted product Mo(CO)[sub 2](MA)[sub 2](NN) (9 or 10). The structures and conformations of 1b and 7 were determined by X-ray diffraction. Both molecules adopt an octahedral geometry with mutually perpendicular trans alkene ligands and each alkene ligand eclipses a N-Mo-CO vector. Each PhMI or MA is oriented so that the central nitrogen or oxygen atom points to a carbonyl group. 1b crystallizes in triclinic space group P1. There are three possible conformations for a trans bis(maleimide) or bis(maleic anhydride) complex (I-III). The results of X-ray and NMR studies indicated that the main conformation of complexes 1-10 is I both in the solid state and in solution.« less

Ten weeks of capoeira progressive training improved cardiovascular parameters in male practitioners.

PubMed

Moreira, Sérgio R; Teixeira-Araujo, Alfredo A; Dos Santos, Aristeu O; Simões, Herbert G

2017-03-01

The present study analyzed the effects of ten weeks of Capoeira progressive training program on the cardiovascular parameters of male practitioners. Participants were assigned into two groups (capoeira, N.=10; 25.4±3.3 years; 24.2±2.2 kg.m2(-1) and Control, N.=08; 29.6±6.3 years; 26.4±4.4 kg.m2(-1)). The Capoeira group performed ten weeks of Capoeira progressive training program, being one session per week lasting 90 minutes each. The control group was instructed to avoid any exercise training program or intense physical activities during the experimental period. The blood pressure (BP), heart rate (HR), and rate pressure product (RPP), as well as HR variability (HRV) indicators were evaluated on resting, before and after intervention. A two-way ANOVA revealed a main effect of group by time interaction to HR (F=6.649, η2=0.379; P=0.02), and HRV indicators (RRi: F=5.752, η2=0.313; rMSSD: F=4.652, η2=0.283; SD1: F=4.694, η2=0.409, and pNN50: F=5.561, η2=0.360; P<0.05). A main effect of time condition was verified for capoeira group (P<0.05) on HR (∆=-6.6±6.0 bpm), RRi (∆=80.1±65.4 ms), rMSSD (∆=14.1±11.6 ms), SD1 (∆=10.0±8.2 ms), and pNN50 (∆=11.3±9.7%). The between groups analysis identified significant differences (P<0.05) for the HR after intervention (capoeira: -8.6±6.9% vs. -0.7±3.9%). The comparison between capoeira vs. control for HRV indicators (RRi: ∆=10.1±8.5% vs. 0.9±7.6%; rMSSD: ∆=37.8±32.9% vs. 2.9±31.3%; pNN50: ∆=96.2±78.7% vs. 0.3±54.1%; and SD1: ∆=37.7±32.9% vs. 6.5±24.4%; respectively) differed to each other (P<0.05). Our findings showed that ten weeks of capoeira progressive training program improves both autonomic and cardiovascular parameters in male practitioners.

Prediction for a Four-Neutron Resonance

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

Shirokov, A. M.; Papadimitriou, G.; Mazur, A. I.

Here, we utilize various ab initio approaches to search for a low-lying resonance in the four-neutron (4n) system using the JISP16 realistic NN interaction. Our most accurate prediction is obtained using a J-matrix extension of the no-core shell model and suggests a 4n resonant state at an energy near E r = 0.8 MeV with a width of approximately Γ = 1.4 MeV.

Prediction for a Four-Neutron Resonance

DOE PAGES

Shirokov, A. M.; Papadimitriou, G.; Mazur, A. I.; ...

2016-10-28

Here, we utilize various ab initio approaches to search for a low-lying resonance in the four-neutron (4n) system using the JISP16 realistic NN interaction. Our most accurate prediction is obtained using a J-matrix extension of the no-core shell model and suggests a 4n resonant state at an energy near E r = 0.8 MeV with a width of approximately Γ = 1.4 MeV.

A Fast Algorithm for Lattice Hyperonic Potentials

NASA Astrophysics Data System (ADS)

Nemura, Hidekatsu; Aoki, Sinya; Doi, Takumi; Gongyo, Shinya; Hatsuda, Tetsuo; Ikeda, Yoichi; Inoue, Takashi; Iritani, Takumi; Ishii, Noriyoshi; Miyamoto, Takaya; Murano, Keiko; Sasaki, Kenji

We describe an efficient algorithm to compute a large number of baryon-baryon interactions from NN to ΞΞ by means of HAL QCD method, which lays the groundwork for the nearly physical point lattice QCD calculation with volume (96a)4 ≈ (8.2 fm)4. Preliminary results of ΛN potential calculated with quark masses corresponding to (mπ, mK) ≈ (146,525) MeV are presented.

Retarding friction versus white noise in the description of heavy ion fusion

NASA Astrophysics Data System (ADS)

Chushnyakova, Maria; Gontchar, Igor

2014-03-01

We performed modeling of the collision of two spherical nuclei resulting in capture. For this aim the stochastic differential equations are used with the white or colored noise and with the instant or retarding friction, respectively. The dissipative forces are proportional to the squared derivative of the strong nucleus-nucleus interaction potential (SnnP). The SnnP is calculated in the framework of the double folding approach with the density-dependent M3Y NN-forces. Calculations performed for 28Si+144Sm reaction show that accounting for the fluctuations typically reduces the capture cross sections by not more than 10%. In contradistinction, the influence of the memory effects is found resulting in about 20% enhancement of the cross section.

Neural Network Technique for Global Ocean Color (Chl-a) Estimates Bridging Multiple Satellite Missions

NASA Astrophysics Data System (ADS)

Garraffo, Z. D.; Nadiga, S.; Krasnopolsky, V.; Mehra, A.; Bayler, E. J.; Kim, H. C.; Behringer, D.

2016-02-01

A Neural Network (NN) technique is used to produce consistent global ocean color estimates, bridging multiple satellite ocean color missions by linking ocean color variability - primarily driven by biological processes - with the physical processes of the upper ocean. Satellite-derived surface variables - sea-surface temperature (SST) and sea-surface height (SSH) fields - are used as signatures of upper-ocean dynamics. The NN technique employs adaptive weights that are tuned by applying statistical learning (training) algorithms to past data sets, providing robustness with respect to random noise, accuracy, fast emulations, and fault-tolerance. This study employs Sea-viewing Wide Field-of-View Sensor (SeaWiFS) chlorophyll-a data for 1998-2010 in conjunction with satellite SSH and SST fields. After interpolating all data sets to the same two-degree latitude-longitude grid, the annual mean was removed and monthly anomalies extracted . The NN technique wass trained for even years of that period and tested for errors and bias for the odd years. The NN output are assessed for: (i) bias, (ii) variability, (iii) root-mean-square error (RMSE), and (iv) cross-correlation. A Jacobian is evaluated to estimate the impact of each input (SSH, SST) on the NN chlorophyll-a estimates. The differences between an ensemble of NNs vs a single NN are examined. After the NN is trained for the SeaWiFS period, the NN is then applied and validated for 2005-2015, a period covered by other satellite missions — the Moderate Resolution Imaging Spectroradiometer (MODIS AQUA) and the Visible Imaging Infrared Radiometer Suite (VIIRS).

Effects of Climate Change on Salmonella Infections

PubMed Central

Akil, Luma; Reddy, Remata S.

2014-01-01

Abstract Background: Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used. Methods: Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN. Results: A seasonal trend in Salmonella infections was observed (p<0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R2=0.554; R2=0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed. Conclusion: There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections. PMID:25496072

The Effect of Personalization on Smartphone-Based Fall Detectors

PubMed Central

Medrano, Carlos; Plaza, Inmaculada; Igual, Raúl; Sánchez, Ángel; Castro, Manuel

2016-01-01

The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training. PMID:26797614

Effect of Ambipolar Diffusion on Ion Abundances in Contracting Protostellar Cores

NASA Astrophysics Data System (ADS)

Ciolek, Glenn E.; Mouschovias, Telemachos Ch.

1998-09-01

Numerical simulations and analytical solutions have established that ambipolar diffusion can reduce the dust-to-gas ratio in magnetically and thermally supercritical cores during the epoch of core formation. We study the effect that this has on the ion chemistry in contracting protostellar cores and present a simplified analytical method that allows one to calculate the ion power-law exponent k (≡d ln ni/d ln nn, where ni and nn are the ion and neutral densities, respectively) as a function of core density. We find that, as in earlier numerical simulations, no single value of k can adequately describe the ion abundance for nn <~ 109 cm-3, a result that is contrary to the ``canonical'' value of k = 1/2 found in previous static equilibrium chemistry calculations and often used to study the effect of ambipolar diffusion in interstellar clouds. For typical cloud and grain parameters, reduction of the abundance of grains results in k > 1/2 during the core formation epoch (densities <~105 cm-3). As a consequence, observations of the degree of ionization in cores could be used, in principle, to determine whether ambipolar diffusion is responsible for core formation in interstellar molecular clouds. For densities >>105 cm-3, k is generally <<1/2.

Communication: Fitting potential energy surfaces with fundamental invariant neural network

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

Shao, Kejie; Chen, Jun; Zhao, Zhiqiang

A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energymore » surfaces for OH{sub 3} and CH{sub 4} were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.« less

Chronic exposure to KATP channel openers results in attenuated glucose sensing in hypothalamic GT1-7 neurons.

PubMed

Haythorne, Elizabeth; Hamilton, D Lee; Findlay, John A; Beall, Craig; McCrimmon, Rory J; Ashford, Michael L J

2016-12-01

Individuals with Type 1 diabetes (T1D) are often exposed to recurrent episodes of hypoglycaemia. This reduces hormonal and behavioural responses that normally counteract low glucose in order to maintain glucose homeostasis, with altered responsiveness of glucose sensing hypothalamic neurons implicated. Although the molecular mechanisms are unknown, pharmacological studies implicate hypothalamic ATP-sensitive potassium channel (K ATP ) activity, with K ATP openers (KCOs) amplifying, through cell hyperpolarization, the response to hypoglycaemia. Although initial findings, using acute hypothalamic KCO delivery, in rats were promising, chronic exposure to the KCO NN414 worsened the responses to subsequent hypoglycaemic challenge. To investigate this further we used GT1-7 cells to explore how NN414 affected glucose-sensing behaviour, the metabolic response of cells to hypoglycaemia and K ATP activity. GT1-7 cells exposed to 3 or 24 h NN414 exhibited an attenuated hyperpolarization to subsequent hypoglycaemic challenge or NN414, which correlated with diminished K ATP activity. The reduced sensitivity to hypoglycaemia was apparent 24 h after NN414 removal, even though intrinsic K ATP activity recovered. The NN414-modified glucose responsiveness was not associated with adaptations in glucose uptake, metabolism or oxidation. K ATP inactivation by NN414 was prevented by the concurrent presence of tolbutamide, which maintains K ATP closure. Single channel recordings indicate that NN414 alters K ATP intrinsic gating inducing a stable closed or inactivated state. These data indicate that exposure of hypothalamic glucose sensing cells to chronic NN414 drives a sustained conformational change to K ATP , probably by binding to SUR1, that results in loss of channel sensitivity to intrinsic metabolic factors such as MgADP and small molecule agonists. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Meaningful public participation in scientific research: How to build an effective site-based long-term education program

NASA Astrophysics Data System (ADS)

Barnett, L.

2013-12-01

Many site-based educators (Wildlife Refuges, nature centers, Cooperative Extension Programs, schools, arboretums) struggle with developing and implementing cohesive long-term scientific monitoring projects into their existing outreach programming. Moreover, projects that are not meaningful to participants often have little or no sustainable long-term impact. Programs proven most effective are those which 1.) engage the participants in the study design and implementation process, 2.) answer a scientific question posed by site leaders; the data collected supports USA-NPN efforts as well as related site management and monitoring questions, 3.) are built into existing outreach and education programs, using phenology as a lens for understanding both natural and cultural history, and 4.) consistently share outcomes and results with the participants. The USA National Phenology Network's (USA-NPN) Education Program provides phenology curriculum and outreach to educators in formal, non-formal, and informal settings. Materials are designed to serve participants in grades 5-12, higher education, and adult learners. Phenology, used as a lens for place-based education, can inform science, environmental, and climate literacy, as well as other subject areas including cultural studies, art, and language arts. The USA-NPN offers consultation with site leaders on how to successfully engage site-based volunteers and students in long-term phenological studies using Nature's Notebook (NN), the professional and citizen science phenology monitoring program. USA-NPN education and educator instruction materials are designed and field-tested to demonstrate how to implement a long-term NN phenology-monitoring program at such sites. These curricula incorporate monitoring for public visitors, long-term volunteers, and school groups, while meeting the goals of USA-NPN and the site, and can be used as a model for other public participation in science programs interested in achieving similar sustainable results. Encouraging long-term data collection, interaction between educators, and offering information about how educators can ask and answer science questions is a key component to meaningfully engaging participants in long-term scientific participation. Evaluation data collected during a two-year initial implementation plan at a demonstration garden site inclusive of these four engagement strategies reflect these findings. Thirty percent of year one participants were very likely to continue NN observations while 48% of year two participants were very likely to continue with the project. Forty percent of participants were very likely to attend an advanced training on NN and 55% of second year participants responded positively. Students better understood phenology's relationship to gardening. Comments included: '...makes you more aware,' 'Very informative... motivate(s) me to record more than...when I hear the first cicada,' and 'Phenology touches everything...brings to light...connecting you already know...tests your new insights [that will] make it more meaningful.' In conclusion, effective education materials holistically and explicitly incorporate personal meaning. Directed content creation helps form an engaged participant base.

NpNn scheme and the saturation of collectivity in the A~=170 and 230 regions

NASA Astrophysics Data System (ADS)

Saha, M.; Sen, S.

1993-02-01

It is shown that the well known phenomenon of the saturation in the B(E20+1-->2+1), as well as the E+21 values near midshell in the even rare-earth and actinide nuclei, can be reproduced in the NpNn scheme through a very simple parametrization in terms of the maximum number of valence protons and neutrons available in the major shells under consideration. This parametrization leads to a product (NpNn)eff which is found to have a more universal character as a structure variable than the usual NpNn.

Ab Initio Calculations of the N-N Bond Dissociation for the Gas-phase RDX and HMX.

PubMed

Liu, Lin-Lin; Liu, Pei-Jin; Hu, Song-Qi; He, Guo-Qiang

2017-01-17

NO 2 fission is a vital factor for 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) decomposition. In this study, the geometry of the gas-phase RDX and HMX molecules was optimized, and the bond order and the bond dissociation energy of the N-N bonds were examined. Moreover, the rate constants of the gas-phase RDX and HMX conformers, concerning the N-N bond dissociation, were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that HMX is more stable than RDX in terms of the N-N bond dissociation, and the conformers stability parameters were as follows: RDXaaa < RDXaae < HMX I < HMX II. In addition, for the RDX conformers, the N-N bond of the pseudo-equatorial positioning of the nitro group was more stable than the N-N bond of the axial positioning of the nitro group, while the results were opposite in the case of the HMX conformers. Moreover, it has been shown that the dissociation rate constant of the N-N bond is influenced by the temperature significantly, thus the rate constants were much lower (<10 -10  s -1 ) when the temperature was less than 1000 K.

Ab Initio Calculations of the N-N Bond Dissociation for the Gas-phase RDX and HMX

PubMed Central

Liu, Lin-lin; Liu, Pei-jin; Hu, Song-qi; He, Guo-qiang

2017-01-01

NO2 fission is a vital factor for 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) decomposition. In this study, the geometry of the gas-phase RDX and HMX molecules was optimized, and the bond order and the bond dissociation energy of the N-N bonds were examined. Moreover, the rate constants of the gas-phase RDX and HMX conformers, concerning the N-N bond dissociation, were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that HMX is more stable than RDX in terms of the N-N bond dissociation, and the conformers stability parameters were as follows: RDXaaa < RDXaae < HMX I < HMX II. In addition, for the RDX conformers, the N-N bond of the pseudo-equatorial positioning of the nitro group was more stable than the N-N bond of the axial positioning of the nitro group, while the results were opposite in the case of the HMX conformers. Moreover, it has been shown that the dissociation rate constant of the N-N bond is influenced by the temperature significantly, thus the rate constants were much lower (<10−10 s−1) when the temperature was less than 1000 K. PMID:28094774

Ab Initio Calculations of the N-N Bond Dissociation for the Gas-phase RDX and HMX

NASA Astrophysics Data System (ADS)

Liu, Lin-Lin; Liu, Pei-Jin; Hu, Song-Qi; He, Guo-Qiang

2017-01-01

NO2 fission is a vital factor for 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) decomposition. In this study, the geometry of the gas-phase RDX and HMX molecules was optimized, and the bond order and the bond dissociation energy of the N-N bonds were examined. Moreover, the rate constants of the gas-phase RDX and HMX conformers, concerning the N-N bond dissociation, were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that HMX is more stable than RDX in terms of the N-N bond dissociation, and the conformers stability parameters were as follows: RDXaaa < RDXaae < HMX I < HMX II. In addition, for the RDX conformers, the N-N bond of the pseudo-equatorial positioning of the nitro group was more stable than the N-N bond of the axial positioning of the nitro group, while the results were opposite in the case of the HMX conformers. Moreover, it has been shown that the dissociation rate constant of the N-N bond is influenced by the temperature significantly, thus the rate constants were much lower (<10-10 s-1) when the temperature was less than 1000 K.

Investigation of the reaction d + d → {sup 2}He + {sup 2}n at the deuteron energy of 15 MeV

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

Konobeevski, E. S., E-mail: konobeev@inr.ru; Zuyev, S. V.; Kasparov, A. A.

An experimental setup for studying the reaction d + d → {sup 2}He + {sup 2}n is described, and the first preliminary results of measurements at a deuteron energy of 15 MeV are presented. The experiment was aimed at determining the energies of quasibound singlet states of two-nucleon systems (nn and pp), these energies being important features of nucleon–nucleon (NN) interaction. The measurements in question were performed at a deuteron beamfrom the U-120 cyclotron of the Skobeltsyn Institute ofNuclear Physics (Moscow State University). Two protons and one of the neutrons fromthe breakup of the dineutron system were detected in themore » experiment. A simulation of the reaction in question and preliminary experimental results reveal the possibility of determining the energy of quasibound singlet states on the basis of the form of the energy spectra of particles originating from their breakup.« less

Luminescent cyclometalated iridium(III) polypyridine indole complexes--synthesis, photophysics, electrochemistry, protein-binding properties, cytotoxicity, and cellular uptake.

PubMed

Lau, Jason Shing-Yip; Lee, Pui-Kei; Tsang, Keith Hing-Kit; Ng, Cyrus Ho-Cheong; Lam, Yun-Wah; Cheng, Shuk-Han; Lo, Kenneth Kam-Wing

2009-01-19

A series of luminescent cyclometalated iridium(III) polypyridine indole complexes, [Ir(N--C)(2)(N--N)](PF(6)) (HN--C = 2-phenylpyridine (Hppy), N--N = 4-((2-(indol-3-yl)ethyl)aminocarbonyl)-4'-methyl-2,2'-bipyridine (bpy-ind) (1a), N--N = 4-((5-((2-(indol-3-yl)ethyl)aminocarbonyl)pentyl)aminocarbonyl)-4'-methyl-2,2'-bipyridine (bpy-C6-ind) (1b); HN--C = 7,8-benzoquinoline (Hbzq), N--N = bpy-ind (2a), N--N = bpy-C6-ind (2b); and HN--C = 2-phenylquinoline (Hpq), N--N = bpy-ind (3a), N--N = bpy-C6-ind (3b)), have been synthesized, characterized, and their photophysical and electrochemical properties and lipophilicity investigated. Photoexcitation of the complexes in fluid solutions at 298 K and in alcohol glass at 77 K resulted in intense and long-lived luminescence (lambda(em) = 540-616 nm, tau(o) = 0.13-5.15 mus). The emission of the complexes has been assigned to a triplet metal-to-ligand charge-transfer ((3)MLCT) (dpi(Ir) --> pi*(N--N)) excited state, probably with some mixing of triplet intraligand ((3)IL) (pi --> pi*) (pq) character for complexes 3a,b. Electrochemical measurements revealed that all the complexes showed an irreversible indole oxidation wave at ca. +1.1 V versus SCE, a quasi-reversible iridium(IV/III) couple at ca. +1.3 V, and a reversible diimine reduction couple at ca. -1.3 V. The interactions of these complexes with an indole-binding protein, bovine serum albumin (BSA), have been studied by emission titrations, and the K(a) values are on the order of 10(4) M(-1). Additionally, the cytotoxicity of the complexes toward human cervix epithelioid carcinoma (HeLa) cells has been examined by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyltetrazolium bromide (MTT) assay. The IC(50) values of the complexes ranged from 1.1 to 6.3 microM, which are significantly smaller than that of cisplatin (30.7 microM) under the same experimental conditions. Furthermore, the cellular uptake of the complexes has been investigated by flow cytometry and laser-scanning confocal microscopy. The microscopy images indicated that complex 3a was localized in the perinuclear region upon interiorization. Temperature-dependence experiments suggested that the internalization of the complex was an energy-requiring process such as endocytosis. This has been confirmed by cellular-uptake experiments involving the luminescent conjugates Ir-BSA and Ir-TF (TF = holo-transferrin), which were prepared by conjugation of the proteins with the complex [Ir(pq)(2)(phen-NCS)](PF(6)) (phen-NCS = 5-isothiocyanato-1,10-phenanthroline).

Factors Controlling Fluxes of Nitrous Oxide (N-N2O) in AN Upland Tropical Forest (atlantic Forest) - Brazil, Rio de Janeiro

NASA Astrophysics Data System (ADS)

Perry, I.; de Mello, W. Z.; McDowell, W. H.

2010-12-01

Atlantic Forest is located along the Brazilian coast and inland to Paraguay and Argentina. It has been largely devastated years ago by anthropogenic activities, such as agriculture and urbanization. Only ten percent of its original area remains (100.000 km2), which is concentrated on high lands. Atlantic Forest is a biodiversity hotspot that receives high nitrogen (N) input through atmospheric deposition in forests of Rio de Janeiro; however, not much is known about the consequences of this N addition. This study has been conducted in the Serra dos Orgaos National Park (SONP - 22.782 km2) located a few kilometers Northeast of Rio de Janeiro Metropolitan Region, Sea Mountain. The forest, characterized as Tropical Moist Forest, is rigorously protected. Vegetation varies along the altitudinal gradient, where the highest peak is at 2,200m asl. Previous studies reported that N atmospheric deposition in SONP varies from 14 to 24 kg ha-1 year-1. The high N deposition on tropical forests increases emission to the atmosphere of N-N2O, a greenhouse gas. There is a lack of N-N2O measurements in tropical forests, mainly in upland tropical forests. We present fluxes of N-N2O from a Brazilian upland tropical forest, and assess the factors controlling N-N2O fluxes. Samples were collected from eight grids (48m2), between 330-451m asl (Subtropical vegetation) and eight grids between 1137-1251m (Montane vegetation), during the dry (July 2008) and wet (Jan-Feb 2009) seasons. Daily, N-N2O (N=372) and soil (N=185) were collected. Nitrous oxide emission was 0,7 (lower altitude) and 0,3 kgN ha-1 year-1 (higher altitude), which is lower than in other upland tropical forests, such as Luquillo Experimental Forest, Puerto Rico, where atmospheric N input (4 kg ha-1 year-1) is not as high as in SONP. Water filled pore space, soil temperature, phosphorus and C:N are the main factors controlling N-N2O fluxes. Manganese was not a good indicator for presence or absence of N-N2O. Higher N-N2O fluxes occurred with a C:N ratio below 16, and also increased with WFPS, soil temperature and phosphorus. Nitrogen availability had little influence on N-N2O fluxes, while phosphorus might be a limiting nutrient for N-N2O production in SONP. *Presenting authot (E-mail: igperry@yahoo.com.br)

Predicting net joint moments during a weightlifting exercise with a neural network model.

PubMed

Kipp, Kristof; Giordanelli, Matthew; Geiser, Christopher

2018-06-06

The purpose of this study was to develop and train a Neural Network (NN) that uses barbell mass and motions to predict hip, knee, and ankle Net Joint Moments (NJM) during a weightlifting exercise. Seven weightlifters performed two cleans at 85% of their competition maximum while ground reaction forces and 3-D motion data were recorded. An inverse dynamics procedure was used to calculate hip, knee, and ankle NJM. Vertical and horizontal barbell motion data were extracted and, along with barbell mass, used as inputs to a NN. The NN was then trained to model the association between the mass and kinematics of the barbell and the calculated NJM for six weightlifters, the data from the remaining weightlifter was then used to test the performance of the NN - this was repeated 7 times with a k-fold cross-validation procedure to assess the NN accuracy. Joint-specific predictions of NJM produced coefficients of determination (r 2 ) that ranged from 0.79 to 0.95, and the percent difference between NN-predicted and inverse dynamics calculated peak NJM ranged between 5% and 16%. The NN was thus able to predict the spatiotemporal patterns and discrete peaks of the three NJM with reasonable accuracy, which suggests that it is feasible to predict lower extremity NJM from the mass and kinematics of the barbell. Future work is needed to determine whether combining a NN model with low cost technology (e.g., digital video and free digitising software) can also be used to predict NJM of weightlifters during field-testing situations, such as practice and competition, with comparable accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

PubMed

Wang, Yearnchee Curtis; Chan, Terence Chee-Hung; Sahakian, Alan Varteres

2018-01-04

Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes. Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue. The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor. The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Simple model for deriving sdg interacting boson model Hamiltonians: 150Nd example

NASA Astrophysics Data System (ADS)

Devi, Y. D.; Kota, V. K. B.

1993-07-01

A simple and yet useful model for deriving sdg interacting boson model (IBM) Hamiltonians is to assume that single-boson energies derive from identical particle (pp and nn) interactions and proton, neutron single-particle energies, and that the two-body matrix elements for bosons derive from pn interaction, with an IBM-2 to IBM-1 projection of the resulting p-n sdg IBM Hamiltonian. The applicability of this model in generating sdg IBM Hamiltonians is demonstrated, using a single-j-shell Otsuka-Arima-Iachello mapping of the quadrupole and hexadecupole operators in proton and neutron spaces separately and constructing a quadrupole-quadrupole plus hexadecupole-hexadecupole Hamiltonian in the analysis of the spectra, B(E2)'s, and E4 strength distribution in the example of 150Nd.

Study of ψ(2 S) production and cold nuclear matter effects in pPb collisions at sqrt{s_{NN}}=5 TeV

NASA Astrophysics Data System (ADS)

Aaij, R.; Abellán Beteta, C.; Adeva, B.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Akar, S.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Alvarez Cartelle, P.; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; An, L.; Anderlini, L.; Andreassi, G.; Andreotti, M.; Andrews, J. E.; Appleby, R. B.; Aquines Gutierrez, O.; Archilli, F.; d'Argent, P.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J. J.; Badalov, A.; Baesso, C.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Battista, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Bel, L. J.; Bellee, V.; Belloli, N.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bertolin, A.; Betti, F.; Bettler, M.-O.; van Beuzekom, M.; Bifani, S.; Billoir, P.; Bird, T.; Birnkraut, A.; Bizzeti, A.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borgheresi, A.; Borghi, S.; Borisyak, M.; Borsato, M.; Bowcock, T. J. V.; Bowen, E.; Bozzi, C.; Braun, S.; Britsch, M.; Britton, T.; Brodzicka, J.; Brook, N. H.; Buchanan, E.; Burr, C.; Bursche, A.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Calvo Gomez, M.; Campana, P.; Campora Perez, D.; Capriotti, L.; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carniti, P.; Carson, L.; Carvalho Akiba, K.; Casse, G.; Cassina, L.; Castillo Garcia, L.; Cattaneo, M.; Cauet, Ch.; Cavallero, G.; Cenci, R.; Charles, M.; Charpentier, Ph.; Chefdeville, M.; Chen, S.; Cheung, S.-F.; Chiapolini, N.; Chrzaszcz, M.; Cid Vidal, X.; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Cogoni, V.; Cojocariu, L.; Collazuol, G.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Coquereau, S.; Corti, G.; Corvo, M.; Couturier, B.; Cowan, G. A.; Craik, D. C.; Crocombe, A.; Cruz Torres, M.; Cunliffe, S.; Currie, R.; D'Ambrosio, C.; Dall'Occo, E.; Dalseno, J.; David, P. N. Y.; Davis, A.; De Aguiar Francisco, O.; De Bruyn, K.; De Capua, S.; De Cian, M.; De Miranda, J. M.; De Paula, L.; De Simone, P.; Dean, C.-T.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Déléage, N.; Demmer, M.; Derkach, D.; Deschamps, O.; Dettori, F.; Dey, B.; Di Canto, A.; Di Ruscio, F.; Dijkstra, H.; Donleavy, S.; Dordei, F.; Dorigo, M.; Dosil Suárez, A.; Dovbnya, A.; Dreimanis, K.; Dufour, L.; Dujany, G.; Dungs, K.; Durante, P.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; Eisenhardt, S.; Eitschberger, U.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Ely, S.; Esen, S.; Evans, H. M.; Evans, T.; Falabella, A.; Färber, C.; Farley, N.; Farry, S.; Fay, R.; Fazzini, D.; Ferguson, D.; Fernandez Albor, V.; Ferrari, F.; Ferreira Rodrigues, F.; Ferro-Luzzi, M.; Filippov, S.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fleuret, F.; Fohl, K.; Fol, P.; Fontana, M.; Fontanelli, F.; Forshaw, D. C.; Forty, R.; Frank, M.; Frei, C.; Frosini, M.; Fu, J.; Furfaro, E.; Gallas Torreira, A.; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; García Pardiñas, J.; Garra Tico, J.; Garrido, L.; Gascon, D.; Gaspar, C.; Gavardi, L.; Gazzoni, G.; Gerick, D.; Gersabeck, E.; Gersabeck, M.; Gershon, T.; Ghez, Ph.; Gianì, S.; Gibson, V.; Girard, O. G.; Giubega, L.; Gligorov, V. V.; Göbel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.; Gotti, C.; Grabalosa Gándara, M.; Graciani Diaz, R.; Granado Cardoso, L. A.; Graugés, E.; Graverini, E.; Graziani, G.; Grecu, A.; Griffith, P.; Grillo, L.; Grünberg, O.; Gui, B.; Gushchin, E.; Guz, Yu.; Gys, T.; Hadavizadeh, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hall, S.; Hamilton, B.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; He, J.; Head, T.; Heijne, V.; Heister, A.; Hennessy, K.; Henrard, P.; Henry, L.; Hernando Morata, J. A.; van Herwijnen, E.; Heß, M.; Hicheur, A.; Hill, D.; Hoballah, M.; Hombach, C.; Hongming, L.; Hulsbergen, W.; Humair, T.; Hushchyn, M.; Hussain, N.; Hutchcroft, D.; Hynds, D.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jawahery, A.; John, M.; Johnson, D.; Jones, C. R.; Joram, C.; Jost, B.; Jurik, N.; Kandybei, S.; Kanso, W.; Karacson, M.; Karbach, T. M.; Karodia, S.; Kecke, M.; Kelsey, M.; Kenyon, I. R.; Kenzie, M.; Ketel, T.; Khairullin, E.; Khanji, B.; Khurewathanakul, C.; Kirn, T.; Klaver, S.; Klimaszewski, K.; Kochebina, O.; Kolpin, M.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Kozeiha, M.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krokovny, P.; Kruse, F.; Krzemien, W.; Kucewicz, W.; Kucharczyk, M.; Kudryavtsev, V.; Kuonen, A. K.; Kurek, K.; Kvaratskheliya, T.; Lacarrere, D.; Lafferty, G.; Lai, A.; Lambert, D.; Lanfranchi, G.; Langenbruch, C.; Langhans, B.; Latham, T.; Lazzeroni, C.; Le Gac, R.; van Leerdam, J.; Lees, J.-P.; Lefèvre, R.; Leflat, A.; Lefrançois, J.; Lemos Cid, E.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Likhomanenko, T.; Liles, M.; Lindner, R.; Linn, C.; Lionetto, F.; Liu, B.; Liu, X.; Loh, D.; Longstaff, I.; Lopes, J. H.; Lucchesi, D.; Lucio Martinez, M.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Lusiani, A.; Machefert, F.; Maciuc, F.; Maev, O.; Maguire, K.; Malde, S.; Malinin, A.; Manca, G.; Mancinelli, G.; Manning, P.; Mapelli, A.; Maratas, J.; Marchand, J. F.; Marconi, U.; Marin Benito, C.; Marino, P.; Marks, J.; Martellotti, G.; Martin, M.; Martinelli, M.; Martinez Santos, D.; Martinez Vidal, F.; Martins Tostes, D.; Massacrier, L. M.; Massafferri, A.; Matev, R.; Mathad, A.; Mathe, Z.; Matteuzzi, C.; Mauri, A.; Maurin, B.; Mazurov, A.; McCann, M.; McCarthy, J.; McNab, A.; McNulty, R.; Meadows, B.; Meier, F.; Meissner, M.; Melnychuk, D.; Merk, M.; Merli, A.; Michielin, E.; Milanes, D. A.; Minard, M.-N.; Mitzel, D. S.; Molina Rodriguez, J.; Monroy, I. A.; Monteil, S.; Morandin, M.; Morawski, P.; Mordà, A.; Morello, M. J.; Moron, J.; Morris, A. B.; Mountain, R.; Muheim, F.; Müller, D.; Müller, J.; Müller, K.; Müller, V.; Mussini, M.; Muster, B.; Naik, P.; Nakada, T.; Nandakumar, R.; Nandi, A.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A. D.; Nguyen-Mau, C.; Niess, V.; Nieswand, S.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; O'Hanlon, D. P.; Oblakowska-Mucha, A.; Obraztsov, V.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Onderwater, C. J. G.; Osorio Rodrigues, B.; Otalora Goicochea, J. M.; Otto, A.; Owen, P.; Oyanguren, A.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Pappalardo, L. L.; Pappenheimer, C.; Parker, W.; Parkes, C.; Passaleva, G.; Patel, G. D.; Patel, M.; Patrignani, C.; Pearce, A.; Pellegrino, A.; Penso, G.; Pepe Altarelli, M.; Perazzini, S.; Perret, P.; Pescatore, L.; Petridis, K.; Petrolini, A.; Petruzzo, M.; Picatoste Olloqui, E.; Pietrzyk, B.; Pikies, M.; Pinci, D.; Pistone, A.; Piucci, A.; Playfer, S.; Plo Casasus, M.; Poikela, T.; Polci, F.; Poluektov, A.; Polyakov, I.; Polycarpo, E.; Popov, A.; Popov, D.; Popovici, B.; Potterat, C.; Price, E.; Price, J. D.; Prisciandaro, J.; Pritchard, A.; Prouve, C.; Pugatch, V.; Puig Navarro, A.; Punzi, G.; Qian, W.; Quagliani, R.; Rachwal, B.; Rademacker, J. H.; Rama, M.; Ramos Pernas, M.; Rangel, M. S.; Raniuk, I.; Raven, G.; Redi, F.; Reichert, S.; dos Reis, A. C.; Renaudin, V.; Ricciardi, S.; Richards, S.; Rihl, M.; Rinnert, K.; Rives Molina, V.; Robbe, P.; Rodrigues, A. B.; Rodrigues, E.; Rodriguez Lopez, J. A.; Rodriguez Perez, P.; Rogozhnikov, A.; Roiser, S.; Romanovsky, V.; Romero Vidal, A.; Ronayne, J. W.; Rotondo, M.; Ruf, T.; Ruiz Valls, P.; Saborido Silva, J. J.; Sagidova, N.; Saitta, B.; Salustino Guimaraes, V.; Sanchez Mayordomo, C.; Sanmartin Sedes, B.; Santacesaria, R.; Santamarina Rios, C.; Santimaria, M.; Santovetti, E.; Sarti, A.; Satriano, C.; Satta, A.; Saunders, D. M.; Savrina, D.; Schael, S.; Schiller, M.; Schindler, H.; Schlupp, M.; Schmelling, M.; Schmelzer, T.; Schmidt, B.; Schneider, O.; Schopper, A.; Schubiger, M.; Schune, M.-H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Semennikov, A.; Sergi, A.; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Siddi, B. G.; Silva Coutinho, R.; Silva de Oliveira, L.; Simi, G.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, E.; Smith, I. T.; Smith, J.; Smith, M.; Snoek, H.; Sokoloff, M. D.; Soler, F. J. P.; Soomro, F.; Souza, D.; Souza De Paula, B.; Spaan, B.; Spradlin, P.; Sridharan, S.; Stagni, F.; Stahl, M.; Stahl, S.; Stefkova, S.; Steinkamp, O.; Stenyakin, O.; Stevenson, S.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Sun, L.; Sutcliffe, W.; Swientek, K.; Swientek, S.; Syropoulos, V.; Szczekowski, M.; Szumlak, T.; T'Jampens, S.; Tayduganov, A.; Tekampe, T.; Tellarini, G.; Teubert, F.; Thomas, C.; Thomas, E.; van Tilburg, J.; Tisserand, V.; Tobin, M.; Todd, J.; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Tournefier, E.; Tourneur, S.; Trabelsi, K.; Traill, M.; Tran, M. T.; Tresch, M.; Trisovic, A.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vacca, C.; Vagnoni, V.; Valenti, G.; Vallier, A.; Vazquez Gomez, R.; Vazquez Regueiro, P.; Vázquez Sierra, C.; Vecchi, S.; van Veghel, M.; Velthuis, J. J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Vieira, D.; Vieites Diaz, M.; Vilasis-Cardona, X.; Volkov, V.; Vollhardt, A.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voß, C.; de Vries, J. A.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, J.; Wang, J.; Ward, D. R.; Watson, N. K.; Websdale, D.; Weiden, A.; Whitehead, M.; Wicht, J.; Wilkinson, G.; Wilkinson, M.; Williams, M.; Williams, M. P.; Williams, M.; Williams, T.; Wilson, F. F.; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S. A.; Wraight, K.; Wright, S.; Wyllie, K.; Xie, Y.; Xu, Z.; Yang, Z.; Yin, H.; Yu, J.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, L.; Zhang, Y.; Zhelezov, A.; Zhokhov, A.; Zhong, L.; Zhukov, V.; Zucchelli, S.

2016-03-01

The production of ψ(2 S) mesons is studied in dimuon final states using proton-lead (pPb) collision data collected by the LHCb detector. The data sample corresponds to an integrated luminosity of 1 .6 nb-1. The nucleon-nucleon centre-of-mass energy of the pPb collisions is sqrt{s_{NN}}=5 TeV. The measurement is performed using ψ(2 S) mesons with transverse momentum less than 14 GeV/ c and rapidity y in the ranges 1 .5 < y < 4 .0 and -5 .0 < y < -2 .5 in the nucleon-nucleon centre-of-mass system. The forward-backward production ratio and the nuclear modification factor are determined for ψ(2 S) mesons. Using the production cross-section results of ψ(2 S) and J/ψ mesons from b-hadron decays, the boverline{b} cross-section in pPb collisions at sqrt{s_{NN}}=5 TeV is obtained. [Figure not available: see fulltext.

Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems.

PubMed

Li, Yuan-Xin; Yang, Guang-Hong

2018-04-01

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

ϒ Production in Heavy-Ion Collisions from the STAR Experiment

NASA Astrophysics Data System (ADS)

Ye, Zaochen; STAR Collaboration

2017-08-01

In these proceedings, we present recent results of ϒ measurements in heavy-ion collisions from the STAR experiment at RHIC. Nuclear modification factors (RAA) for ϒ (1 S) and ϒ (1 S + 2 S + 3 S) in U+U collisions at √{sNN } = 193 GeV are measured through the di-electron channel and compared to those in Au+Au collisions at √{sNN } = 200 GeV and Pb+Pb collisions at √{sNN } = 2.76 TeV. The ratio between the ϒ (2 S + 3 S) and ϒ (1 S) yields in Au+Au collisions at √{sNN } = 200 GeV is measured in the di-muon channel and compared to those in p+p collisions and in Pb+Pb collisions at √{sNN } = 2.76 TeV. Prospects for future ϒ measurements with the STAR experiment are also discussed.

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

Majidpour, Mostafa; Qiu, Charlie; Chu, Peter

Three algorithms for the forecasting of energy consumption at individual EV charging outlets have been applied to real world data from the UCLA campus. Out of these three algorithms, namely k-Nearest Neighbor (kNN), ARIMA, and Pattern Sequence Forecasting (PSF), kNN with k=1, was the best and PSF was the worst performing algorithm with respect to the SMAPE measure. The advantage of PSF is its increased robustness to noise by substituting the real valued time series with an integer valued one, and the advantage of NN is having the least SMAPE for our data. We propose a Modified PSF algorithm (MPSF)more » which is a combination of PSF and NN; it could be interpreted as NN on integer valued data or as PSF with considering only the most recent neighbor to produce the output. Some other shortcomings of PSF are also addressed in the MPSF. Results show that MPSF has improved the forecast performance.« less

Infrared spectroscopy and density functional calculations on titanium-dinitrogen complexes

NASA Astrophysics Data System (ADS)

Yoo, Hae-Wook; Choi, Changhyeok; Cho, Soo Gyeong; Jung, Yousung; Choi, Myong Yong

2018-04-01

Titanium-nitrogen complexes were generated by laser ablated titanium (Ti) atoms and N2 gas molecules in this study. These complexes were isolated on the pre-deposited solid Ar matrix on the pre-cooled KBr window (T ∼ 5.4 K), allowing infrared spectra to be measured. Laser ablation experiments with 15N2 isotope provided distinct isotopic shifts in the infrared spectra that strongly implicated the formation of titanium-nitrogen complexes, Ti(NN)x. Density functional theory (DFT) calculations were employed to investigate the molecular structures, electronic ground state, relative energies, and IR frequencies of the anticipated Ti(NN)x complexes. Based on laser ablation experiments and DFT calculations, we were able to assign multiple Ti(NN)x (x = 1-6) species. Particularly, Ti(NN)5 and Ti(NN)6, which have high nitrogen content, may serve as good precursors in preparing polynitrogens.

Study of Z boson production in pPb collisions at √{sNN} = 5.02TeV

NASA Astrophysics Data System (ADS)

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L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; Filipovic, N.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Mastrolorenzo, L.; Miné, P.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Merlin, J. A.; Skovpen, K.; van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Schael, S.; Schulte, J. F.; Verlage, T.; Weber, H.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Hoehle, F.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Nehrkorn, A.; Nowack, A.; Nugent, I. M.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Asin, I.; Bartosik, N.; Behnke, O.; Behrens, U.; Borras, K.; Burgmeier, A.; Campbell, A.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Dooling, S.; Dorland, T.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Flucke, G.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Korol, I.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Nayak, A.; Ntomari, E.; Perrey, H.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Seitz, C.; Spannagel, S.; Stefaniuk, N.; Trippkewitz, K. D.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Gonzalez, D.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Nowatschin, D.; Ott, J.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Pietsch, N.; Poehlsen, J.; Rathjens, D.; Sander, C.; Scharf, C.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Schumann, S.; Schwandt, J.; Sola, V.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; Colombo, F.; de Boer, W.; Descroix, A.; Dierlamm, A.; Fink, S.; Frensch, F.; Friese, R.; Giffels, M.; Gilbert, A.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Kornmayer, A.; Lobelle Pardo, P.; Maier, B.; Mildner, H.; Mozer, M. U.; Müller, T.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Bencze, G.; Hajdu, C.; Hazi, A.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Bartók, M.; Makovec, A.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Choudhury, S.; Mal, P.; Mandal, K.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Malhotra, S.; Naimuddin, M.; Nishu, N.; Ranjan, K.; Sharma, R.; Sharma, V.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutta, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Roy, A.; Roy, D.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Banerjee, S.; Bhowmik, S.; Chatterjee, R. M.; Dewanjee, R. K.; Dugad, S.; Ganguly, S.; Ghosh, S.; Guchait, M.; Gurtu, A.; Jain, Sa.; Kole, G.; Kumar, S.; Mahakud, B.; Maity, M.; Majumder, G.; Mazumdar, K.; Mitra, S.; Mohanty, G. B.; Parida, B.; Sarkar, T.; Sur, N.; Sutar, B.; Wickramage, N.; Chauhan, S.; Dube, S.; Kapoor, A.; Kothekar, K.; Sharma, S.; Bakhshiansohi, H.; Behnamian, H.; Etesami, S. M.; Fahim, A.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; de Filippis, N.; de Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Venditti, R.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Cappello, G.; Chiorboli, M.; Costa, S.; di Mattia, A.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Viliani, L.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Primavera, F.; Calvelli, V.; Ferro, F.; Lo Vetere, M.; Monge, M. R.; Robutti, E.; Tosi, S.; Brianza, L.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Malvezzi, S.; Manzoni, R. A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; di Guida, S.; Esposito, M.; Fabozzi, F.; Iorio, A. O. M.; Lanza, G.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Benato, L.; Bisello, D.; Boletti, A.; Carlin, R.; Checchia, P.; Dall'Osso, M.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Passaseo, M.; Pazzini, J.; Pegoraro, M.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Ventura, S.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Foà, L.; Giassi, A.; Grippo, M. 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W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Benvenuti, A. C.; Dahmes, B.; Evans, A.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bartek, R.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Knowlton, D.; Kravchenko, I.; Meier, F.; Monroy, J.; Ratnikov, F.; Siado, J. E.; Snow, G. R.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira de Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Zhang, J.; Bhattacharya, S.; Hahn, K. A.; Kubik, A.; Low, J. F.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Ling, T. Y.; Liu, B.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Zuranski, A.; Malik, S.; Barker, A.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Jung, K.; Kumar, A.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Chou, J. P.; Contreras-Campana, E.; Ferencek, D.; Gershtein, Y.; Halkiadakis, E.; Heindl, M.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Lath, A.; Nash, K.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Foerster, M.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; de Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Krutelyov, V.; Mueller, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Rose, A.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Undleeb, S.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Wood, J.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Sarangi, T.; Savin, A.; Sharma, A.; Smith, N.; Smith, W. H.; Taylor, D.; Verwilligen, P.; Woods, N.

2016-08-01

The production of Z bosons in pPb collisions at √{sNN} = 5.02 TeV is studied by the CMS experiment via the electron and muon decay channels. The inclusive cross section is compared to pp collision predictions, and found to scale with the number of elementary nucleon-nucleon collisions. The differential cross sections as a function of the Z boson rapidity and transverse momentum are measured. Though they are found to be consistent within uncertainty with theoretical predictions both with and without nuclear effects, the forward-backward asymmetry suggests the presence of nuclear effects at large rapidities. These results provide new data for constraining nuclear parton distribution functions.

Air Quality Data for Metals 1970 Through 1974 from the ...

EPA Pesticide Factsheets

... Ln f i i Ln ' F P 1 .nin • • t LI ! p Ln p p i Ln ' Ln T Ln • Ln • Ln ! i i Ln • Ln ! Ln • Ln • Ln • • • t .nn7 > .ni^ • LD ' .nin i t t i .PZT ! .nn7 • ,nn= • ?ND. QTR. ...

Applying an efficient K-nearest neighbor search to forest attribute imputation

Treesearch

Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek

2006-01-01

This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...

Efficient Analysis of Complex Structures

NASA Technical Reports Server (NTRS)

Kapania, Rakesh K.

2000-01-01

Last various accomplishments achieved during this project are : (1) A Survey of Neural Network (NN) applications using MATLAB NN Toolbox on structural engineering especially on equivalent continuum models (Appendix A). (2) Application of NN and GAs to simulate and synthesize substructures: 1-D and 2-D beam problems (Appendix B). (3) Development of an equivalent plate-model analysis method (EPA) for static and vibration analysis of general trapezoidal built-up wing structures composed of skins, spars and ribs. Calculation of all sorts of test cases and comparison with measurements or FEA results. (Appendix C). (4) Basic work on using second order sensitivities on simulating wing modal response, discussion of sensitivity evaluation approaches, and some results (Appendix D). (5) Establishing a general methodology of simulating the modal responses by direct application of NN and by sensitivity techniques, in a design space composed of a number of design points. Comparison is made through examples using these two methods (Appendix E). (6) Establishing a general methodology of efficient analysis of complex wing structures by indirect application of NN: the NN-aided Equivalent Plate Analysis. Training of the Neural Networks for this purpose in several cases of design spaces, which can be applicable for actual design of complex wings (Appendix F).

Temperature-Dependent Second Shell Interference in the First Shell Analysis of Crystalline InP X-ray Absorption Spectroscopy Data

NASA Astrophysics Data System (ADS)

Schnohr, Claudia S.; Araujo, Leandro L.; Ridgway, Mark C.

2014-09-01

Analysing only the first nearest neighbour (NN) scattering signal is a commonly used and often successful way to treat extended X-ray absorption fine structure data. However, using temperature-dependent measurements of InP as an example, we demonstrate how this approach can lead to erroneous first NN structural parameters in systems with a weak first but strong second NN scatterer. In such cases, particularly low temperature data may suffer from an overlap of first and second NN scattering signals caused by the Fourier transformation (FT) even if the dominant peaks appear to be well separated. The first NN structural parameters then vary as a function of the FT settings if only the first NN scattering contribution is considered in the analysis. Although this variation is small, it can also lead to significant differences in other calculated properties such as the Einstein temperature. We demonstrate that these variations can be avoided either by choosing an appropriate FT window or by including the scattering contributions of higher shells in the analysis. The latter is achieved by a path fitting approach and yields structural parameters independent of the FT settings used.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

Effect of Polymorphisms at Codon 146 of the Goat PRNP Gene on Susceptibility to Challenge with Classical Scrapie by Different Routes.

PubMed

Papasavva-Stylianou, Penelope; Simmons, Marion Mathieson; Ortiz-Pelaez, Angel; Windl, Otto; Spiropoulos, John; Georgiadou, Soteria

2017-11-15

This report presents the results of experimental challenges of goats with scrapie by both the intracerebral (i.c.) and oral routes, exploring the effects of polymorphisms at codon 146 of the goat PRNP gene on resistance to disease. The results of these studies illustrate that while goats of all genotypes can be infected by i.c. challenge, the survival distribution of the animals homozygous for asparagine at codon 146 was significantly shorter than those of animals of all other genotypes (chi-square value, 10.8; P = 0.001). In contrast, only those animals homozygous for asparagine at codon 146 (NN animals) succumbed to oral challenge. The results also indicate that any cases of infection in non-NN animals can be detected by the current confirmatory test (immunohistochemistry), although successful detection with the rapid enzyme-linked immunosorbent assay (ELISA) was more variable and dependent on the polymorphism. Together with data from previous studies of goats exposed to infection in the field, these data support the previously reported observations that polymorphisms at this codon have a profound effect on susceptibility to disease. It is concluded that only animals homozygous for asparagine at codon 146 succumb to scrapie under natural conditions. IMPORTANCE In goats, like in sheep, there are PRNP polymorphisms that are associated with susceptibility or resistance to scrapie. However, in contrast to the polymorphisms in sheep, they are more numerous in goats and may be restricted to certain breeds or geographical regions. Therefore, eradication programs must be specifically designed depending on the identification of suitable polymorphisms. An initial analysis of surveillance data suggested that such a polymorphism in Cypriot goats may lie in codon 146. In this study, we demonstrate experimentally that NN animals are highly susceptible after i.c. inoculation. The presence of a D or S residue prolonged incubation periods significantly, and prions were detected in peripheral tissues only in NN animals. In oral challenges, prions were detected only in NN animals, and the presence of a D or S residue at this position conferred resistance to the disease. This study provides an experimental transmission model for assessing the genetic susceptibility of goats to scrapie. © Crown copyright 2017.

Artificial astrocytes improve neural network performance.

PubMed

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial Astrocytes Improve Neural Network Performance

PubMed Central

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Central institutional review board review for an academic trial network.

PubMed

Kaufmann, Petra; O'Rourke, P Pearl

2015-03-01

Translating discoveries into therapeutics is often delayed by lengthy start-up periods for multicenter clinical trials. One cause of delay can be multiple institutional review board (IRB) reviews of the same protocol. When developing the Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT; hereafter, NN), the National Institute of Neurological Disorders and Stroke (NINDS) established a central IRB (CIRB) based at Massachusetts General Hospital, the academic medical center that received the NN clinical coordinating center grant. The 25 NN sites, located at U.S. academic institutions, agreed to required CIRB use for NN trials. To delineate roles and establish legal relationships between the NN sites and the CIRB, the CIRB executed reliance agreements with the sites and their affiliates that hold federalwide assurance for the protection of human subjects (FWA); this took, on average, 84 days. The first NN protocol reviewed by the CIRB achieved full approval to allow participant enrollment within 56 days and went from grant award to the first patient visit in less than four months. The authors describe anticipated challenges related to institutional oversight responsibilities versus regulatory CIRB review as well as unanticipated challenges related to working with complex organizations that include multiple FWA-holding affiliates. The authors anticipate that CIRB use will decrease NN trial start-up time and thus promote efficient trial implementation. They plan to collect data on timelines and costs associated with CIRB use. The NINDS plans to promote CIRB use in future initiatives.

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

NASA Astrophysics Data System (ADS)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Flavor-dependent eigenvolume interactions in a hadron resonance gas

NASA Astrophysics Data System (ADS)

Alba, P.; Vovchenko, V.; Gorenstein, M. I.; Stoecker, H.

2018-06-01

Eigenvolume effects in the hadron resonance gas (HRG) model are studied for experimental hadronic yields in nucleus-nucleus collisions. If particle eigenvolumes are different for different hadron species, the excluded volume HRG (EV-HRG) improves fits to multiplicity data. In particular, using different mass-volume relations for strange and non-strange hadrons we observe a remarkable improvement in the quality of the fits. This effect appears to be rather insensitive to other details in the schemes employed in the EV-HRG. We show that the parameters found from fitting the data of the ALICE Collaboration in central Pb+Pb collisions at the collision energy √{sNN } = 2.76 TeV entail the same improvement for all centralities at the same collision energy, and for the RHIC and SPS data at lower collision energies. Our findings are put in the context of recent fits of lattice QCD results.

Evaluation of normalization methods for cDNA microarray data by k-NN classification

PubMed Central

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-01-01

Background Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Results Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Conclusion Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics. PMID:16045803

Evaluation of normalization methods for cDNA microarray data by k-NN classification.

PubMed

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-07-26

Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.

Tri-Service Construction Guide Specifications

DTIC Science & Technology

1992-04-01

Equipment 11474 NN 9102 11757 Radiographic Darkroom Equipment 11476 VA 8202 11471 Revolving Darkroom Doors 11494 VA 8201 11491 Hydrotherapy Equipment...11494 NN 9102 11716 Hydrotherapy Equipment 11500 CE 9105 11500 Air Pollution Control 11500 NS 9103 13255 Cleaning for Process Piping Systems 11600 NN...Doors (11471) 4 11494 - Hydrotherapy Equipment (11491) 0 0 11500 - Air Pollution Control 0 11500 - Cleaning for Process Piping Systems (13255) 0 11600

Minimum Expected Risk Estimation for Near-neighbor Classification

DTIC Science & Technology

2006-04-01

We consider the problems of class probability estimation and classification when using near-neighbor classifiers, such as k-nearest neighbors ( kNN ...estimate for weighted kNN classifiers with different prior information, for a broad class of risk functions. Theory and simulations show how significant...the difference is compared to the standard maximum likelihood weighted kNN estimates. Comparisons are made with uniform weights, symmetric weights

Portable Language-Independent Adaptive Translation from OCR. Phase 1

DTIC Science & Technology

2009-04-01

including brute-force k-Nearest Neighbors ( kNN ), fast approximate kNN using hashed k-d trees, classification and regression trees, and locality...achieved by refinements in ground-truthing protocols. Recent algorithmic improvements to our approximate kNN classifier using hashed k-D trees allows...recent years discriminative training has been shown to outperform phonetic HMMs estimated using ML for speech recognition. Standard ML estimation

Translation of the assembling trajectory by preorganisation: a study of the magnetic properties of 1D polymeric unpaired electrons immobilised on a discrete nanoscopic scaffold.

PubMed

Praveen, Vakayil K; Yamamoto, Yohei; Fukushima, Takanori; Tsunobuchi, Yoshihide; Nakabayashi, Koji; Ohkoshi, Shin-ichi; Kato, Kenichi; Takata, Masaki; Aida, Takuzo

2015-01-25

A nitronyl nitroxide (NN)-appended hexabenzocoronene (HBC(NN)), when allowed to coassemble with bis(hexafluoroacetylacetonato)cobalt(II), forms a coaxial nanotubular architecture featuring NN-Co(II) coordinated copolymer chains immobilised on the outer and inner nanotube surfaces. Upon lowering the temperature, this nanotube has enhanced magnetic susceptibility below 10 K.

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

PubMed

Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

2006-04-01

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

PubMed

Shen, Lin; Yang, Weitao

2018-03-13

Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes.

Reconstruction of paleoceanographic significance in the Atlantic, Pacific, and the Indian Ocean during the Neogene based on calcareous nannofossil productivity and coccolith size distribution of Reticulofenestra - with special reference to formation of petroleum source rocks

NASA Astrophysics Data System (ADS)

Pratiwi, S. D.; Sato, T.; Ovinda, O.; Syavitri, D.

2017-12-01

We studied in detail the calcareous nannofossils assemblages from the ODP Sites of the western Pacific, Bahama Bank of Caribbean Sea, northwestern Pacific, Equatorial Pacific and the Indian Ocean to reconstruct the Cenozoic paleoceanographic evolution and correlate with the global events. The absolute abundant of coccolith (number/g) is gradually increased from NN6 throughout NN19 Zone, while the relative abundance of Discoaster is decreased in the Pacific Ocean. The size of Reticulofenestra increased five times throughout the section. However, it drastically decreased in NN8-10 (8.80 Ma), NN12-13 (5.40 Ma), NN14-NN15 (3.75 Ma), NN17/NN18 (2.52 Ma) and in NN19 Zone (0.80 Ma) in the western Pacific site. The characteristic of eutrophication condition determined by the high productivity of coccolith and the drastic decrease of the maximum size of Reticulofenestra are strongly related to the appearance of nutricline in the sea surface ocean. On the basis of the relationship between the changes of maximum sizes of Reticulofenestra and nutrient condition, these eutrophication events are clearly traceable in the western Pacific, Bahama Bank of Caribbean Sea, northwestern Pacific, Equatorial Pacific and the Indian Ocean. Two paleoceanographic events found in 8.80 Ma and 3.75 Ma are interpreted as a change to high nutrient condition resulted in the intensification of Asian Monsoon and closure of Panama Isthmus (Fig.). The upwelling of nutrient-rich oceanic waters may give rise to exceptionally high organic productivity. Organic carbon- rich facies accumulate preferentially during major intensification episodes. The timing of high productivity of coccolith during the middle to late Miocene is related and applicable to the formation of petroleum source rock and traceable to the Japan, marginal eastern North Pacific and California oil sites. This study suggests that the timing of the collapse of sea surface condition or eutrophication condition (8.00 Ma to 10.00 Ma) is correlated to the timing of formation petroleum source rocks in Circum Pacific based on calcareous nannofossils study.

Differential MS2 Interaction with Food Contact Surfaces Determined by Atomic Force Microscopy and Virus Recovery.

PubMed

Shim, J; Stewart, D S; Nikolov, A D; Wasan, D T; Wang, R; Yan, R; Shieh, Y C

2017-12-15

Enteric viruses are recognized as major etiologies of U.S. foodborne infections. These viruses are easily transmitted via food contact surfaces. Understanding virus interactions with surfaces may facilitate the development of improved means for their removal, thus reducing transmission. Using MS2 coliphage as a virus surrogate, the strength of virus adhesion to common food processing and preparation surfaces of polyvinyl chloride (PVC) and glass was assessed by atomic force microscopy (AFM) and virus recovery assays. The interaction forces of MS2 with various surfaces were measured from adhesion peaks in force-distance curves registered using a spherical bead probe preconjugated with MS2 particles. MS2 in phosphate-buffered saline (PBS) demonstrated approximately 5 times less adhesion force to glass (0.54 nN) than to PVC (2.87 nN) ( P < 0.0001). This was consistent with the virus recovery data, which showed 1.4-fold fewer virus PFU recovered from PVC than from glass after identical inoculations and 24 h of cold storage. The difference in adhesion was ascribed to both intrinsic chemical characteristics and the substrate surface porosity (smooth glass versus porous PVC). Incorporating a surfactant micellar solution of sodium dodecyl sulfate (SDS) into the PBS reduced the adhesion force for PVC (∼0 nN) and consistently increased virus recovery by 19%. With direct and indirect evidence of virus adhesion, this study illustrated a two-way assessment of virus adhesion for the initial evaluation of potential means to mitigate virus adhesion to food contact surfaces. IMPORTANCE The spread of foodborne viruses is likely associated with their adhesive nature. Virus attachment on food contact surfaces has been evaluated by quantitating virus recoveries from inoculated surfaces. This study aimed to evaluate the microenvironment in which nanometer-sized viruses interact with food contact surfaces and to compare the virus adhesion differences using AFM. The virus surrogate MS2 demonstrated less adhesion force to glass than to PVC via AFM, with the force-contributing factors including the intrinsic nature and the topography of the contact surfaces. This adhesion finding is consistent with the virus recoveries, which were determined indirectly. Greater numbers of viruses were recovered from glass than from PVC, after application at the same levels. The stronger MS2 adhesion onto PVC could be interrupted by incorporating a surfactant during the interaction between the virus and the contact surface. This study increases our understanding of the virus adhesion microenvironment and indicates ways to mitigate virus adhesion onto contact surfaces. This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply.

Interaction of electron neutrino with LSD detector

NASA Astrophysics Data System (ADS)

Ryazhskaya, O. G.; Semenov, S. V.

2016-06-01

The interaction of electron neutrino flux, originating in the rotational collapse mechanism on the first stage of Supernova burst, with the LSD detector components, such as 56Fe (a large amount of this metal is included in as shielding material) and liquid scintillator barNnH2n+2, is being investigated. Both charged and neutral channels of neutrino reaction with 12barN and 56Fe are considered. Experimental data, giving the possibility to extract information for nuclear matrix elements calculation are used. The number of signals, produced in LSD by the neutrino pulse of Supernova 1987A is determined. The obtained results are in good agreement with experimental data.

Semimicroscopic, Lane-consistent nucleon-nucleus optical model potential up to 200 MeV

NASA Astrophysics Data System (ADS)

Bauge, Eric; Delaroche, Jean-Paul; Girod, Michel

2000-10-01

Our semimicroscopic optical model potential (E. Bauge et al., Phys. Rev. C 58), 1118 (1998). is re-evaluated in order to obtain a Lane-consistent description of (p,p), (n,n) and (p,n IAS) elastic scattering and reaction observables. The re-assessed nuclear matter interaction (which includes sizable renormalizations of the isovector potentials) is folded with microscopic HFB nuclear densities, producing OMPs that are free of adjustable parameters for nuclei with A >= 40. With Lane-consistency of the interaction, and the predictive nature of our HFB calculations, this scheme can be used to calculate observables for nuclei far from the stability line with good predictivity.

Peripheral halo-functionalization in [Cu(N^N)(P^P)]+ emitters: influence on the performances of light-emitting electrochemical cells.

PubMed

Brunner, Fabian; Martínez-Sarti, Laura; Keller, Sarah; Pertegás, Antonio; Prescimone, Alessandro; Constable, Edwin C; Bolink, Henk J; Housecroft, Catherine E

2016-09-27

A series of heteroleptic [Cu(N^N)(P^P)][PF 6 ] complexes is described in which P^P = bis(2-(diphenylphosphino)phenyl)ether (POP) or 4,5-bis(diphenylphosphino)-9,9-dimethylxanthene (xantphos) and N^N = 4,4'-diphenyl-6,6'-dimethyl-2,2'-bipyridine substituted in the 4-position of the phenyl groups with atom X (N^N = 1 has X = F, 2 has X = Cl, 3 has X = Br, 4 has X = I; the benchmark N^N ligand with X = H is 5). These complexes have been characterized by multinuclear NMR spectroscopy, mass spectrometry, elemental analyses and cyclic voltammetry; representative single crystal structures are also reported. The solution absorption spectra are characterized by high energy bands (arising from ligand-centred transitions) which are red-shifted on going from X = H to X = I, and a broad metal-to-ligand charge transfer band with λ max in the range 387-395 nm. The ten complexes are yellow emitters in solution and yellow or yellow-orange emitters in the solid-state. For a given N^N ligand, the solution photoluminescence (PL) spectra show no significant change on going from [Cu(N^N)(POP)] + to [Cu(N^N)(xantphos)] + ; introducing the iodo-functionality into the N^N domain leads to a red-shift in λ compared to the complexes with the benchmark N^N ligand 5. In the solid state, [Cu(1)(POP)][PF 6 ] and [Cu(1)(xantphos)][PF 6 ] (fluoro-substituent) exhibit the highest PL quantum yields (74 and 25%, respectively) with values of τ 1/2 = 11.1 and 5.8 μs, respectively. Light-emitting electrochemical cells (LECs) with [Cu(N^N)(P^P)][PF 6 ] complexes in the emissive layer have been tested. Using a block-wave pulsed current driving mode, the best performing device employed [Cu(1)(xantphos)] + and this showed a maximum luminance (Lum max ) of 129 cd m -2 and a device lifetime (t 1/2 ) of 54 h; however, the turn-on time (time to reach Lum max ) was 4.1 h. Trends in performance data reveal that the introduction of fluoro-groups is beneficial, but that the incorporation of heavier halo-substituents leads to poor devices, probably due to a detrimental effect on charge transport; LECs with the iodo-functionalized N^N ligand 4 failed to show any electroluminescence after 50 h.

Hybrid NN/SVM Computational System for Optimizing Designs

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2009-01-01

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Infrared Fingerprints of nN → σ*NH Hyperconjugation in Hydrazides.

PubMed

Andrade, Laize A F; Silla, Josué M; Cormanich, Rodrigo A; Freitas, Matheus P

2017-12-01

An earlier study demonstrated that hyperconjugation operates in hydrazides by analyzing the N-H stretching mode in gas phase infrared (IR) spectroscopy, and then observing two very distinct bands corresponding to isolated isomers experiencing or not the n N → σ* N-H electron delocalization. The present work reports a chemical method to obtain insight on the hyperconjugation in hydrazide derivatives from solution IR spectroscopy. The analogous amides did not show a ν N-H red-shifted band, as the electron donor orbital in the above hyperconjugative interaction does not exist. In addition, the effect of electron withdrawing groups bonded to a nitrogen atom, namely the trifluoroacetyl and the methanesulfonyl groups, was analyzed on the conformational isomerism and on the ability to induce a stronger hyperconjugation in the resulting compounds.

Secure and Efficient k-NN Queries⋆

PubMed Central

Asif, Hafiz; Vaidya, Jaideep; Shafiq, Basit; Adam, Nabil

2017-01-01

Given the morass of available data, ranking and best match queries are often used to find records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the financial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efficient. PMID:29218333

Antiparallel spin does not always contain more information

NASA Astrophysics Data System (ADS)

Ghosh, Sibasish; Roy, Anirban; Sen, Ujjwal

2001-01-01

We show that the Bloch vectors lying on any great circle comprise the largest set SL for which the parallel states \\|n-->,n-->> can always be exactly transformed into the antiparallel states \\|n-->,-n-->>. Thus more information about n--> is not extractable from \\|n-->,-n-->> than from \\|n-->,n-->> by any measuring strategy, for n-->∈SL. Surprisingly this most general transformation reduces to just a flip operation on the second particle. We also show here that a probabilistic exact parallel to antiparallel transformation is not possible if the corresponding antiparallel states span the whole Hilbert space of the two qubits. These considerations allow us to generalize a conjecture of Gisin and Popescu [Phys. Rev. Lett. 83, 432 (1999)].

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Compositional effects in Ru, Pd, Pt, and Rh-doped mesoporous tantalum oxide catalysts for ammonia synthesis.

PubMed

Yue, Chaoyang; Qiu, Longhui; Trudeau, Michel; Antonelli, David

2007-06-11

A series of early metal-promoted Ru-, Pd-, Pt-, and Rh-doped mesoporous tantalum oxide catalysts were synthesized using a variety of dopant ratios and dopant precursors, and the effects of these parameters on the catalytic activity of NH3 synthesis from H2 and N2 were explored. Previous studies on this system supported an unprecedented mechanism in which N-N cleavage occurred at the Ta sites rather than on Ru. The results of the present study showed, for all systems, that Ba is a better promoter than Cs or La and that the nitrate is a superior precursor for Ba than the isopropoxide or the hydroxide. 15N-labeling studies showed that residual nitrate functions as the major ammonia source in the first hour but that it does not account for the ammonia produced after the nitrate is completely consumed. Ru3(CO)12 proved to be a better Ru precursor than RuCl(3).3H2O, and an almost linear increase in activity with increasing Ru loading level was observed at 350 degrees C (623 K). However, at 175 degrees C (448 K), the increase in Ru had no effect on the reaction rate. Pd functioned with comparable rates to Ru, while Pt and Rh functioned far less efficiently. The surprising activities for the Pd-doped catalysts, coupled with XPS evidence for low-valent Ta in this catalyst system, support a mechanism in which cleavage of the N-N triple bond occurs on Ta rather than the precious metal because the Ea value for N-N cleavage on Pd is 2.5 times greater than that for Ru, and the 9.3 kJ mol-1 Ea value measured previously for the Ru system suggests that N-N cleavage cannot occur at the Ru surface.

A comparison of neural network-based predictions of foF2 with the IRI-2012 model at conjugate points in Southeast Asia

NASA Astrophysics Data System (ADS)

Wichaipanich, Noraset; Hozumi, Kornyanat; Supnithi, Pornchai; Tsugawa, Takuya

2017-06-01

This paper presents the development of Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at three ionosonde stations near the magnetic equator of Southeast Asia. Two of these stations including Chiang Mai (18.76°N, 98.93°E, dip angle 12.7°N) and Kototabang (0.2°S, 100.3°E, dip angle 10.1°S) are at the conjugate points while Chumphon (10.72°N, 99.37°E, dip angle 3.0°N) station is near the equator. To produce the model, the feed forward network with backpropagation algorithm is applied. The NN is trained with the daily hourly values of foF2 during 2004-2012, except 2009, and the selected input parameters, which affect the foF2 variability, include day number (DN), hour number (HR), solar zenith angle (C), geographic latitude (θ), magnetic inclination (I), magnetic declination (D) and angle of meridian (M) relative to the sub-solar point, the 7-day mean of F10.7 (F10.7_7), the 81-day mean of SSN (SSN_81) and the 2-day mean of Ap (Ap_2). The foF2 data of 2009 and 2013 are then used for testing the NN model during the foF2 interpolation and extrapolation, respectively. To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI-2012 (CCIR and URSI options) model are compared. In general, the results show the same trends in foF2 variation between the models (NN and IRI-2012) and the observations in that they are higher during the day and lower at night. Besides, the results demonstrate that the proposed NN model can predict the foF2 values more closely during daytime than during nighttime as supported by the lower RMSE values during daytime (0.5 ≤ RMSE ≤ 1.0 for Chumphon and Kototabang, 0.7 ≤ RMSE ≤ 1.2 at Chiang Mai) and with the highest levels during nighttime (0.8 ≤ RMSE ≤ 1.5 for Chumphon and Kototabang, 1.2 ≤ RMSE ≤ 2.0 at Chiang Mai). Furthermore, the NN model predicts the foF2 values more accurately than the IRI model at the three sites on average, as clearly seen on the yearly RMSE averages. The RMSE values of NN model are lower than those of both CCIR and URSI options, and in terms of the yearly percentage improvements, the NN model gives improvement of around 10-15% in 2009 and 10% in 2013 for Chiang Mai, 20-25% in 2009 and 5-10% in 2013 for Chumphon, and around 18-25% in 2009 and 20-30% in 2013 at Kototabang. Although the NN model predicts the foF2 values closely to the observed data and produces more accurate prediction than the IRI models, in some cases, the IRI model performs better than the NN model. Hence, there is still room for further improvement of the proposed NN model.

Effects of defects on thermal decomposition of HMX via ReaxFF molecular dynamics simulations.

PubMed

Zhou, Ting-Ting; Huang, Feng-Lei

2011-01-20

Effects of molecular vacancies on the decomposition mechanisms and reaction dynamics of condensed-phase β-HMX at various temperatures were studied using ReaxFF molecular dynamics simulations. Results show that three primary initial decomposition mechanisms, namely, N-NO(2) bond dissociation, HONO elimination, and concerted ring fission, exist at both high and lower temperatures. The contribution of the three mechanisms to the initial decomposition of HMX is influenced by molecular vacancies, and the effects vary with temperature. At high temperature (2500 K), molecular vacancies remarkably promote N-N bond cleavage and concerted ring breaking but hinder HONO formation. N-N bond dissociation and HONO elimination are two primary competing reaction mechanisms, and the former is dominant in the initial decomposition. Concerted ring breaking of condensed-phase HMX is not favored at high temperature. At lower temperature (1500 K), the most preferential initial decomposition pathway is N-N bond dissociation followed by the formation of NO(3) (O migration), although all three mechanisms are promoted by molecular vacancies. The promotion effect on concerted ring breaking is considerable at lower temperature. Products resulting from concerted ring breaking appear in the defective system but not in the perfect crystal. The mechanism of HONO elimination is less important at lower temperature. We also estimated the reaction rate constant and activation barriers of initial decomposition with different vacancy concentrations. Molecular vacancies accelerate the decomposition of condensed-phase HMX by increasing the reaction rate constant and reducing activation barriers.

Prime Contract Awards Alphabetically by Contractor, by State or Country, and Place, FY 88. Part 19. (Synertech Associates, Inc.-Tucker Equipment Co., Inc.)

DTIC Science & Technology

1988-01-01

1-r. -wOq* o 0 v-* vvqtq "V so 400960 I- WO La U," 5 U),U 1, 0 wwo Uft www owm flm w woqa : C0 NNO N "NN N - 0 000WIWo m’ V 0 U, -4-4- mmmmm0 -4 1-4...Om 0 000000000iini 50-4 I N N4 N -44-4 -4 -4-4 -4 N -4 -4 NN4 Nl NN N NN 50000 I -4 -4 -4 - -4 -4 -4 -4 -4 4-4 -4 4 -f -4 44444- 50000 5 -4 N 4 N -4 -4...PP=P 100.4 1 0000 00 ,00-0 1 0000 00 I00w I -4-4-4-4 .4- 500-t 1 444- .4.4 500(4 5 N N -4 N C4NN 4 N (4N N N NNNNC1 -4.4 N4 NINNNNNNNN SO)ON 5 CC4 NNN

Environmental Life Cycle Techniques for New Weapons Acquisition Systems

DTIC Science & Technology

2004-09-01

Amount) Total grenade War Caracterisation factors (MJ/nn) Unit (Impact indicator) Total grenade War Total of all compartments MJ 59200 82700... Caracterisation factors (Kg CFC11 eq/nn) Unit (Impact indicator) Total grenade War Total of all compartments kg CFC-11 0,000429 0,00046 Remaining...Substance Compartment Unit (Amount) Total grenade War Caracterisation factors (Kg C2H2/nn) Unit (Impact indicator) Total grenade War Total of all

New Brunswick Laboratory. Progress report, October 1995--September 1996

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

NONE

Fiscal year (FY) 1996 was a very good year for New Brunswick Laboratory (NBL), whose major sponsor is the Office of Safeguards and Security (NN-51) in the US Department of Energy (DOE), Office of Nonproliferation and National Security, Office of Security Affairs. Several projects pertinent to the NBL mission were completed, and NBL`s interactions with partners and customers were encouraging. Among the partners with which NBL interacted in this report period were the International Atomic Energy Agency (IAEA), NN-51. Environmental Program Group of the DOE Chicago Operations Office, International Safeguards Project Office, Waste Isolation Pilot Plant (WIPP), Ukraine Working Group,more » Fissile Materials Assurance Working Group, National Institute of Standards and Technology (NIST), Nuclear Regulatory Commission (NRC), Institute for Reference Materials and Measurements (IRMM) in Belgium, Brazilian/Argentine Agency for Accounting and Control of Nuclear Materials (ABACC), Lockheed Idaho Technologies Company, and other DOE facilities and laboratories. NBL staff publications, participation in safeguards assistance and other nuclear programs, development of new reference materials, involvement in the updating and refinement of DOE documents, service in enhancing the science education of others, and other related activities enhanced NBL`s status among DOE laboratories and facilities. Noteworthy are the facts that NBL`s small inventory of nuclear materials is accurately accounted for, and, as in past years, its materials and human resources were used in peaceful nuclear activities worldwide.« less

Evaluation of adhesion force between functionalized microbeads and protein-coated stainless steel using shear-flow-induced detachment.

PubMed

Mercier-Bonin, Muriel; Adoue, Mathieu; Zanna, Sandrine; Marcus, Philippe; Combes, Didier; Schmitz, Philippe

2009-10-01

Spherical microbeads functionalized with two types of chemical groups (NH(2), OH) were chosen as a simplified bacterial model, in order to elucidate the role of macromolecular interactions between specific biopolymers and 316 L stainless steel, in the frame of biofilm formation in the marine environment. NH(2) microbeads were used in their native form or after covalent binding to BSA or different representative poly-amino acids. OH microbeads were used in their native form. Adhesion force between microbeads and bare or BSA-coated stainless steel was quantified at nanoscale. Shear-flow-induced detachment experiments were combined with a simplified version of a theoretical model, based on the balance of hydrodynamic forces and torque exerted on microbeads. A maximal adhesion force of 27.6+/-8.5 nN was obtained for BSA-coated NH(2) microbeads. The high reactivity of OH functional groups was assessed (adhesion force of 15.6+/-4.8 nN for large microbeads). When charge-conducting stainless steel was coated with BSA, adhesion force was significantly lower than the one estimated with the bare surface, probably due to an increase in hydrophilic surface properties or suppression of charge transfer. The mechanism for microbead detachment was established (mainly rolling). The flow chamber and the associated theoretical modelling were demonstrated to be a relevant approach to quantify nanoscale forces between interacting surfaces.

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

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

Lin, Jian; Hamidouche, Khaled; Zheng, Jie

2015-08-05

Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemicmore » evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.« less

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbedAllah, Loai

2016-12-22

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai

2016-12-01

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

FMRI 3D registration based on Fourier space subsets using neural networks.

PubMed

Freire, Luis C; Gouveia, Ana R; Godinho, Fernando M

2010-01-01

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Centrality dependence of chemical freeze-out parameters from net-proton and net-charge fluctuations using a hadron resonance gas model

NASA Astrophysics Data System (ADS)

Adak, Rama Prasad; Das, Supriya; Ghosh, Sanjay K.; Ray, Rajarshi; Samanta, Subhasis

2017-07-01

We estimate chemical freeze-out parameters in Hadron Resonance Gas (HRG) and Excluded Volume HRG (EVHRG) models by fitting the experimental information of net-proton and net-charge fluctuations measured in Au + Au collisions by the STAR Collaboration at the BNL Relativistic Heavy Ion Collider (RHIC). We observe that chemical freeze-out parameters obtained from lower and higher order fluctuations are almost the same for √{sNN}>27 GeV, but tend to deviate from each other at lower √{sNN}. Moreover, these separations increase with decrease of √{sNN}, and for a fixed √{sNN} increase towards central collisions. Furthermore, we observe an approximate scaling behavior of (μB/T ) /(μB/T)central with (Npart) /(Npart)central for the parameters estimated from lower order fluctuations for 11.5 ≤√{sNN}≤200 GeV. Scaling is violated for the parameters estimated from higher order fluctuations for √{sNN}=11.5 and 19.6 GeV. It is observed that the chemical freeze-out parameter, which can describe σ2/M of net protons very well in all energies and centralities, cannot describe the s σ equally well, and vice versa.

The Miocene "Pteropod event" in the SW part of the Central Paratethys (Medvednica Mt., northern Croatia)

NASA Astrophysics Data System (ADS)

Bošnjak, Marija; Sremac, Jasenka; Vrsaljko, Davor; Aščić, Šimun; Bosak, Luka

2017-08-01

Deep marine Miocene deposits exposed sporadically in the Medvednica Mt. (northern Croatia) comprise pelagic organisms such as coccolithophores, planktic foraminifera and pteropods. The pteropod fauna from yellow marls at the Vejalnica locality (central part of Medvednica Mt.) encompasses abundant specimens of Vaginella austriaca Kittl, 1886, accompanied with scarce Clio fallauxi (Kittl, 1886). Calcareous nannoplankton points to the presence of NN5 nannozone at this locality. Highly fossiliferous grey marls at the Marija Bistrica locality (north-eastern area of Medvednica Mt.) comprise limacinid pteropods: Limacina valvatina (Reuss, 1867), L. gramensis (Rasmussen, 1968) and Limacina sp. Late Badenian (NN5 to NN6 nannozone) age of these marls is presumed on the basis of coccolithophores. Most of the determined pteropods on species level, except V. austriaca have been found and described from this region for the first time. New pteropod records from Croatia point to two pteropod horizons coinciding with the Badenian marine transgressions in Central Paratethys. These pteropod assemblages confirm the existence of W-E marine connection ("Transtethyan Trench Corridor") during the Badenian NN5 nannozone. Limacinids point to the possible immigration of the "North Sea fauna" through a northern European marine passage during the Late Badenian (end of NN5-beginning of NN6 zone), as previously presumed by some other authors.

Nuclear matter effects on J /ψ production in asymmetric Cu + Au collisions at √{sNN}=200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Alexander, J.; Alfred, M.; Aoki, K.; Apadula, N.; Aramaki, Y.; Asano, H.; Atomssa, E. T.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Bai, X.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Bathe, S.; Baublis, V.; Baumann, C.; Baumgart, S.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belmont, R.; Berdnikov, A.; Berdnikov, Y.; Bing, X.; Black, D.; Blau, D. S.; Bok, J. S.; Boyle, K.; Brooks, M. L.; Bryslawskyj, J.; Buesching, H.; Bumazhnov, V.; Butsyk, S.; Campbell, S.; Chen, C.-H.; Chi, C. Y.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choi, S.; Christiansen, P.; Chujo, T.; Cianciolo, V.; Citron, Z.; Cole, B. A.; Cronin, N.; Crossette, N.; Csanád, M.; Csörgő, T.; Datta, A.; Daugherity, M. S.; David, G.; Deblasio, K.; Dehmelt, K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Ding, L.; Dion, A.; Do, J. H.; Drapier, O.; Drees, A.; Drees, K. A.; Durham, J. M.; Durum, A.; D'Orazio, L.; Engelmore, T.; Enokizono, A.; En'yo, H.; Esumi, S.; Eyser, K. O.; Fadem, B.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fukao, Y.; Gainey, K.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, A.; Garishvili, I.; Ge, H.; Giordano, F.; Glenn, A.; Gong, X.; Gonin, M.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grosse Perdekamp, M.; Gu, Y.; Gunji, T.; Guragain, H.; Hachiya, T.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Han, S. Y.; Hanks, J.; Hasegawa, S.; Hashimoto, K.; Hayano, R.; He, X.; Hemmick, T. K.; Hester, T.; Hill, J. C.; Hollis, R. S.; Homma, K.; Hong, B.; Hoshino, T.; Huang, J.; Huang, S.; Ichihara, T.; Ikeda, Y.; Imai, K.; Imazu, Y.; Inaba, M.; Iordanova, A.; Isenhower, D.; Isinhue, A.; Ivanishchev, D.; Jacak, B. V.; Jeon, S. J.; Jezghani, M.; Jia, J.; Jiang, X.; Johnson, B. M.; Joo, E.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kamin, J.; Kanda, S.; Kang, B. H.; Kang, J. H.; Kang, J. S.; Kapustinsky, J.; Kawall, D.; Kazantsev, A. V.; Key, J. A.; Khachatryan, V.; Khandai, P. K.; Khanzadeev, A.; Kihara, K.; Kijima, K. M.; Kim, C.; Kim, D. H.; Kim, D. J.; Kim, E.-J.; Kim, H.-J.; Kim, M.; Kim, Y.-J.; Kim, Y. K.; Kistenev, E.; Klatsky, J.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kofarago, M.; Komkov, B.; Koster, J.; Kotchetkov, D.; Kotov, D.; Krizek, F.; Kurita, K.; Kurosawa, M.; Kwon, Y.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Lee, D. M.; Lee, G. H.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, S. H.; Leitch, M. J.; Leitgab, M.; Lewis, B.; Li, X.; Lim, S. H.; Liu, M. X.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Makek, M.; Manion, A.; Manko, V. I.; Mannel, E.; Maruyama, T.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Meles, A.; Mendoza, M.; Meredith, B.; Miake, Y.; Mibe, T.; Mignerey, A. C.; Miller, A. J.; Milov, A.; Mishra, D. K.; Mitchell, J. T.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Montuenga, P.; Moon, T.; Morrison, D. P.; Moskowitz, M.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Mwai, A.; Nagae, T.; Nagamiya, S.; Nagle, J. L.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nattrass, C.; Netrakanti, P. K.; Nihashi, M.; Niida, T.; Nouicer, R.; Novak, T.; Novitzky, N.; Nyanin, A. S.; O'Brien, E.; Ogilvie, C. A.; Oide, H.; Okada, K.; Orjuela Koop, J. D.; Oskarsson, A.; Ozaki, H.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, S.; Park, S. K.; Pate, S. F.; Patel, L.; Patel, M.; Peng, J.-C.; Perepelitsa, D. V.; Perera, G. D. N.; Peressounko, D. Yu.; Perry, J.; Petti, R.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Purschke, M. L.; Qu, H.; Rak, J.; Ravinovich, I.; Read, K. F.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Riveli, N.; Roach, D.; Rolnick, S. D.; Rosati, M.; Rowan, Z.; Rubin, J. G.; Ryu, M. S.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sako, H.; Samsonov, V.; Sarsour, M.; Sato, S.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Sekiguchi, Y.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shaver, A.; Shein, I.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Singh, B. K.; Singh, C. P.; Singh, V.; Skolnik, M.; Slunečka, M.; Solano, S.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Soumya, M.; Sourikova, I. V.; Stankus, P. W.; Steinberg, P.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Stone, M. R.; Sugitate, T.; Sukhanov, A.; Sumita, T.; Sun, J.; Sziklai, J.; Takahara, A.; Taketani, A.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tennant, E.; Timilsina, A.; Todoroki, T.; Tomášek, M.; Torii, H.; Towell, M.; Towell, R.; Towell, R. S.; Tserruya, I.; van Hecke, H. W.; Vargyas, M.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Virius, M.; Vrba, V.; Vznuzdaev, E.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Whitaker, S.; Wolin, S.; Woody, C. L.; Wysocki, M.; Xia, B.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yanovich, A.; Yokkaichi, S.; Yoon, I.; You, Z.; Younus, I.; Yushmanov, I. E.; Zajc, W. A.; Zelenski, A.; Zhou, S.; Phenix Collaboration

2014-12-01

We report on J /ψ production from asymmetric Cu + Au heavy-ion collisions at √{sNN}=200 GeV at the Relativistic Heavy Ion Collider at both forward (Cu-going direction) and backward (Au-going direction) rapidities. The nuclear modification of J /ψ yields in Cu + Au collisions in the Au-going direction is found to be comparable to that in Au + Au collisions when plotted as a function of the number of participating nucleons. In the Cu-going direction, J /ψ production shows a stronger suppression. This difference is comparable in magnitude and has the same sign as the difference expected from shadowing effects due to stronger low-x gluon suppression in the larger Au nucleus.

Switchable diode effect in oxygen vacancy-modulated SrTiO3 single crystal

NASA Astrophysics Data System (ADS)

Pan, Xinqiang; Shuai, Yao; Wu, Chuangui; Luo, Wenbo; Sun, Xiangyu; Zeng, Huizhong; Bai, Xiaoyuan; Gong, Chaoguan; Jian, Ke; Zhang, Lu; Guo, Hongliang; Tian, Benlang; Zhang, Wanli

2017-09-01

SrTiO3 (STO) single crystal wafer was annealed in vacuum, and co-planar metal-insulator-metal structure of Pt/Ti/STO/Ti/Pt were formed by sputtering Pt/Ti electrodes onto the surface of STO after annealing. The forming-free resistive switching behavior with self-compliance property was observed in the sample. The sample showed switchable diode effect, which is explained by a simple model that redistribution of oxygen vacancies (OVs) under the external electric field results in the formation of n-n+ junction or n+-n junction (n donated n-type semiconductor; n+ donated heavily doped n-type semiconductor). The self-compliance property is also interpreted by the formation of n-n+/n+-n junction caused by the migration of the OVs under the electric field.

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at √{sNN}=2.76 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aimo, I.; Aiola, S.; Ajaz, M.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Armesto, N.; Arnaldi, R.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Bach, M.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Baltasar Dos Santos Pedrosa, F.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blanco, F.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Böttger, S.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Cavicchioli, C.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Chunhui, Z.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa Del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; de, S.; de Caro, A.; de Cataldo, G.; de Cuveland, J.; de Falco, A.; de Gruttola, D.; De Marco, N.; de Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; D'Erasmo, G.; di Bari, D.; di Mauro, A.; di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Dobrowolski, T.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Engel, H.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Eschweiler, D.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Felea, D.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Grosse-Oetringhaus, J. F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gulkanyan, H.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hansen, A.; Harris, J. W.; Hartmann, H.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hilden, T. E.; Hillemanns, H.; Hippolyte, B.; Hosokawa, R.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacobs, P. M.; Jadlovska, S.; Jahnke, C.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, K. H.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, B.; Kim, D. W.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobayashi, T.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Kral, J.; Králik, I.; Kravčáková, A.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kugathasan, T.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Legrand, I.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Luz, P. H. F. N. D.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Martynov, Y.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Masui, H.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; McDonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Morando, M.; Moreira de Godoy, D. A.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Murray, S.; Musa, L.; Musinsky, J.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pajares, C.; Pal, S. K.; Pan, J.; Pandey, A. K.; Pant, D.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Peitzmann, T.; Pereira da Costa, H.; Pereira de Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Real, J. S.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rivetti, A.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salgado, C. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sanchez Castro, X.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Seo, J.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Soltz, R.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Sultanov, R.; Šumbera, M.; Symons, T. J. M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tangaro, M. A.; Tapia Takaki, J. D.; Tarantola Peloni, A.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; van der Maarel, J.; van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Wang, Y.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Wessels, J. P.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.; Alice Collaboration

2016-03-01

We report on results obtained with the event-shape engineering technique applied to Pb-Pb collisions at √{sNN}=2.76 TeV. By selecting events in the same centrality interval, but with very different average flow, different initial-state conditions can be studied. We find the effect of the event-shape selection on the elliptic flow coefficient v2 to be almost independent of transverse momentum pT, which is as expected if this effect is attributable to fluctuations in the initial geometry of the system. Charged-hadron, -pion, -kaon, and -proton transverse momentum distributions are found to be harder in events with higher-than-average elliptic flow, indicating an interplay between radial and elliptic flow.

A theoretical investigation on the neutral Cu(I) phosphorescent complexes with azole-based and phosphine mixed ligand

NASA Astrophysics Data System (ADS)

Ding, Xiao-Li; Shen, Lu; Zou, Lu-Yi; Ma, Ming-Shuo; Ren, Ai-Min

2018-04-01

A theoretical study on a series of neutral heteroleptic Cu(I) complexes with different azole-pyridine-based N^N ligands has been presented to get insight into the effect of various nitrogen atoms in the azole ring on photophysical properties. The results reveal that the highest occupied molecular orbital (HOMO) levels and the emission wavelengths of these complexes are mainly governed by the nitrogen atom number in azole ring. With the increasing number of nitrogen atom , the electron density distribution of HOMO gradually extend from the N^N ligand to the whole molecule, meanwhile, the improved contribution from Cu(d) orbits in HOMO results in an effective mixing of various charge transfermodes, and hence, the fast radiative decay(kr) and the slow non-radiative decay rate(knr) are achieved. The photoluminescence quantum yield (PLQY) show an apparent dependence on the nitrogen atom number in the five-membered nitrogen heterocycles. However, the increasing number of nitrogen atoms is not necessary for increasing PLQY. The complex 3 with 1,2,4-triazole-pyridine-based N^N ligands is considered to be a potential emitter with high phosphorescence efficiency. Finally, we hope that our investigations will contribute to systematical understanding and guiding for material molecular engineering.

Measurement of charged jet production cross sections and nuclear modification in p-Pb collisions at √{sNN} = 5.02 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aimo, I.; Aiola, S.; Ajaz, M.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Armesto, N.; Arnaldi, R.; Aronsson, T.; Arsene, I. C.; Arslandok, M.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Bach, M.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Baltasar Dos Santos Pedrosa, F.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biswas, S.; Bjelogrlic, S.; Blanco, F.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botje, M.; Botta, E.; Böttger, S.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Buxton, J. T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Cavicchioli, C.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Chunhui, Z.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; D'Erasmo, G.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Dobrowolski, T.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Engel, H.; Erazmus, B.; Erhardt, F.; Eschweiler, D.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Felea, D.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Gomez Ramirez, A.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Grosse-Oetringhaus, J. F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gulkanyan, H.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hanratty, L. D.; Hansen, A.; Harris, J. W.; Hartmann, H.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hilden, T. E.; Hillemanns, H.; Hippolyte, B.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Ionita, C.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacobs, P. M.; Jahnke, C.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, K. H.; Khan, M. M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, B.; Kim, D. W.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobayashi, T.; Kobdaj, C.; Kofarago, M.; Köhler, M. K.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Kox, S.; Koyithatta Meethaleveedu, G.; Kral, J.; Králik, I.; Kravčáková, A.; Krelina, M.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kugathasan, T.; Kuhn, C.; Kuijer, P. G.; Kulakov, I.; Kumar, J.; Kumar, L.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Legrand, I.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loggins, V. R.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Luz, P. H. F. N. D.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manceau, L.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Martynov, Y.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Masui, H.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Morando, M.; Moreira De Godoy, D. A.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Müller, H.; Mulligan, J. D.; Munhoz, M. G.; Murray, S.; Musa, L.; Musinsky, J.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pajares, C.; Pal, S. K.; Pan, J.; Pandey, A. K.; Pant, D.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Paul, B.; Peitzmann, T.; Pereira Da Costa, H.; Pereira De Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Real, J. S.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reicher, M.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rivetti, A.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salgado, C. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sanchez Castro, X.; Šándor, L.; Sandoval, A.; Sano, M.; Santagati, G.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Seeder, K. S.; Seger, J. E.; Sekiguchi, Y.; Selyuzhenkov, I.; Senosi, K.; Seo, J.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Soltz, R.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Sultanov, R.; Šumbera, M.; Symons, T. J. M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Takahashi, J.; Tanaka, N.; Tangaro, M. A.; Tapia Takaki, J. D.; Tarantola Peloni, A.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Wang, Y.; Watanabe, D.; Weber, M.; Weber, S. G.; Wessels, J. P.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yamaguchi, Y.; Yang, H.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2015-10-01

Charged jet production cross sections in p-Pb collisions at √{sNN} = 5.02 TeV measured with the ALICE detector at the LHC are presented. Using the anti-kT algorithm, jets have been reconstructed in the central rapidity region from charged particles with resolution parameters R = 0.2 and R = 0.4. The reconstructed jets have been corrected for detector effects and the underlying event background. To calculate the nuclear modification factor, RpPb, of charged jets in p-Pb collisions, a pp reference was constructed by scaling previously measured charged jet spectra at √{ s} = 7 TeV. In the transverse momentum range 20 ≤p T , ch jet ≤ 120 GeV / c, RpPb is found to be consistent with unity, indicating the absence of strong nuclear matter effects on jet production. Major modifications to the radial jet structure are probed via the ratio of jet production cross sections reconstructed with the two different resolution parameters. This ratio is found to be similar to the measurement in pp collisions at √{ s} = 7 TeV and to the expectations from PYTHIA pp simulations and NLO pQCD calculations at √{sNN} = 5.02 TeV.

The production of π±, K±, p and p¯ in p-Pb collisions at sNN = 5.02 TeV

NASA Astrophysics Data System (ADS)

Tabassam, U.; Ali, Y.; Suleymanov, M.; Bhatti, A. S.; Ajaz, M.

2018-06-01

In this study, we are reporting comprehensive results on π±, K±, p and p¯ production in the transverse momentum range of 0 < pT < 4 GeV/c at midrapidity of 0 < y < 0.5 GeV/c, in p-Pb collisions at sNN = 5.02 TeV. HIJING 1.0 and UrQMD 3.4 event generators are used to perform simulations and the results are compared with the ALICE and RHIC data. It is observed from the comparison that the yields for the baryons are more complex compared to the mesons and the complexity in baryons is due to the striping dynamics (spectators, leading particles of projectiles) of inner nucleus protons and neutrons. Though all the mesons could be produced during the interaction, they have maximum longitudinal momentum pL; baryons and mesons could be produced as a result of decay of massive baryon-resonances. Yields for the π± mesons are greater than the yield for the K± mesons. These are the well-known results from the RHIC data, which stated that the Cronin Effect is mainly due to π± mesons that can be produced as a result of multi-particle inner nucleus cascade. There exists the regions where yields for the K± mesons and baryons are same that may be due to the appearance of parton nature. The code used in simulation includes the parton dynamics earlier than it is included in the experiment.

Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

NASA Technical Reports Server (NTRS)

Bosworth, John

2006-01-01

A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

Weighted Parzen Windows for Pattern Classification

DTIC Science & Technology

1994-05-01

Nearest-Neighbor Rule The k-Nearest-Neighbor ( kNN ) technique is nonparametric, assuming nothing about the distribution of the data. Stated succinctly...probabilities P(wj I x) from samples." Raudys and Jain [20:255] advance this interpretation by pointing out that the kNN technique can be viewed as the...34Parzen window classifier with a hyper- rectangular window function." As with the Parzen-window technique, the kNN classifier is more accurate as the

Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

NASA Astrophysics Data System (ADS)

Li, C.-D.; Tan, D.-R.; Jiang, F.-J.

2018-04-01

We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

Pseudo-scalar pi N coupling and relativistic proton-nucleus scattering

NASA Technical Reports Server (NTRS)

Gross, Franz; Maung, Khin Maung; Tjon, J. A.; Townsend, L. W.; Wallace, S. J.

1988-01-01

Relativistic p-Ca-40 elastic scattering observables are calculated using relativistic NN amplitudes obtained from the solution of a two-body relativistic equation in which one particle is kept on its mass-shell. Results at 200 MeV are presented for two sets of NN amplitudes, one with pure pseudo-vector coupling for the pion and another with a 25 percent admixture of pseudo-scaling coupling. Both give a very good fit to the positive energy on-shell NN data. Differences between the predictions of these two models (which are shown to be due only to the differences in their corresponding negative energy amplitudes) provide a measure of the uncertainty in contructing Dirac optical potentials from NN amplitudes.

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

The use of novel biodegradable, optically active and nanostructured poly(amide-ester-imide) as a polymer matrix for preparation of modified ZnO based bionanocomposites

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

Abdolmaleki, Amir, E-mail: abdolmaleki@cc.iut.ac.ir; Nanotechnology and Advanced Materials Institute, Isfahan University of Technology, Isfahan 84156-83111, Islamic Republic of Iran; Mallakpour, Shadpour, E-mail: mallak@cc.iut.ac.ir

Highlights: Black-Right-Pointing-Pointer A novel biodegradable and nanostructured PAEI based on two amino acids, was synthesized. Black-Right-Pointing-Pointer ZnO nanoparticles were modified via two different silane coupling agents. Black-Right-Pointing-Pointer PAEI/modified ZnO BNCs were synthesized through ultrasound irradiation. Black-Right-Pointing-Pointer ZnO particles were dispersed homogeneously in PAEI matrix on nanoscale. Black-Right-Pointing-Pointer The effect of ZnO nanoparticles on the properties of synthesized polymer was examined. -- Abstract: A novel biodegradable and nanostructured poly(amide-ester-imide) (PAEI) based on two different amino acids, was synthesized via direct polycondensation of biodegradable N,N Prime -bis[2-(methyl-3-(4-hydroxyphenyl)propanoate)]isophthaldiamide and N,N Prime -(pyromellitoyl)-bis-L-phenylalanine diacid. The resulting polymer was characterized by FT-IR, {sup 1}H NMR,more » specific rotation, elemental analysis, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) analysis. The synthesized polymer showed good thermal stability with nano and sphere structure. Then PAEI/ZnO bionanocomposites (BNCs) were fabricated via interaction of pure PAEI and ZnO nanoparticles. The surface of ZnO was modified with two different silane coupling agents. PAEI/ZnO BNCs were studied and characterized by FT-IR, XRD, UV/vis, FE-SEM and TEM. The TEM and FE-SEM results indicated that the nanoparticles were dispersed homogeneously in PAEI matrix on nanoscale. Furthermore the effect of ZnO nanoparticle on the thermal stability of the polymer was investigated with TGA and DSC technique.« less

TEMIS UV product validation using NILU-UV ground-based measurements in Thessaloniki, Greece

NASA Astrophysics Data System (ADS)

Zempila, Melina-Maria; van Geffen, Jos H. G. M.; Taylor, Michael; Fountoulakis, Ilias; Koukouli, Maria-Elissavet; van Weele, Michiel; van der A, Ronald J.; Bais, Alkiviadis; Meleti, Charikleia; Balis, Dimitrios

2017-06-01

This study aims to cross-validate ground-based and satellite-based models of three photobiological UV effective dose products: the Commission Internationale de l'Éclairage (CIE) erythemal UV, the production of vitamin D in the skin, and DNA damage, using high-temporal-resolution surface-based measurements of solar UV spectral irradiances from a synergy of instruments and models. The satellite-based Tropospheric Emission Monitoring Internet Service (TEMIS; version 1.4) UV daily dose data products were evaluated over the period 2009 to 2014 with ground-based data from a Norsk Institutt for Luftforskning (NILU)-UV multifilter radiometer located at the northern midlatitude super-site of the Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (LAP/AUTh), in Greece. For the NILU-UV effective dose rates retrieval algorithm, a neural network (NN) was trained to learn the nonlinear functional relation between NILU-UV irradiances and collocated Brewer-based photobiological effective dose products. Then the algorithm was subjected to sensitivity analysis and validation. The correlation of the NN estimates with target outputs was high (r = 0. 988 to 0.990) and with a very low bias (0.000 to 0.011 in absolute units) proving the robustness of the NN algorithm. For further evaluation of the NILU NN-derived products, retrievals of the vitamin D and DNA-damage effective doses from a collocated Yankee Environmental Systems (YES) UVB-1 pyranometer were used. For cloud-free days, differences in the derived UV doses are better than 2 % for all UV dose products, revealing the reference quality of the ground-based UV doses at Thessaloniki from the NILU-UV NN retrievals. The TEMIS UV doses used in this study are derived from ozone measurements by the SCIAMACHY/Envisat and GOME2/MetOp-A satellite instruments, over the European domain in combination with SEVIRI/Meteosat-based diurnal cycle of the cloud cover fraction per 0. 5° × 0. 5° (lat × long) grid cells. TEMIS UV doses were found to be ˜ 12.5 % higher than the NILU NN estimates but, despite the presence of a visually apparent seasonal pattern, the R2 values were found to be robustly high and equal to 0.92-0.93 for 1588 all-sky coincidences. These results significantly improve when limiting the dataset to cloud-free days with differences of 0.57 % for the erythemal doses, 1.22 % for the vitamin D doses, and 1.18 % for the DNA-damage doses, with standard deviations of the order of 11-13 %. The improvement of the comparative statistics under cloud-free cases further testifies to the importance of the appropriate consideration of the contribution of clouds in the UV radiation reaching the Earth's surface. For the urban area of Thessaloniki, with highly variable aerosol, the weakness of the implicit aerosol information introduced to the TEMIS UV dose algorithm was revealed by comparison of the datasets to aerosol optical depths at 340 nm as reported by a collocated CIMEL sun photometer, operating in Thessaloniki at LAP/AUTh as part of the NASA Aerosol Robotic Network.

Satellite retrievals of Karenia brevis harmful algal blooms in the West Florida shelf using neural networks and impacts of temporal variabilities

NASA Astrophysics Data System (ADS)

El-Habashi, Ahmed; Duran, Claudia M.; Lovko, Vincent; Tomlinson, Michelle C.; Stumpf, Richard P.; Ahmed, Sam

2017-07-01

We apply a neural network (NN) technique to detect/track Karenia brevis harmful algal blooms (KB HABs) plaguing West Florida shelf (WFS) coasts from Visible-Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previously KB HABs detection primarily relied on the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite, depending on its remote sensing reflectance signal at the 678-nm chlorophyll fluorescence band (Rrs678) needed for normalized fluorescence height and related red band difference retrieval algorithms. VIIRS, MODIS-A's successor, does not have a 678-nm channel. Instead, our NN uses Rrs at 486-, 551-, and 671-nm VIIRS channels to retrieve phytoplankton absorption at 443 nm (a). The retrieved a images are next filtered by applying limits, defined by (i) low Rrs551-nm backscatter and (ii) a minimum a value associated with KB HABs. The filtered residual images are then converted to show chlorophyll-a concentrations [Chla] and KB cell counts. VIIRS retrievals using our NN and five other retrieval algorithms were compared and evaluated against numerous in situ measurements made over the four-year 2012 to 2016 period, for which VIIRS data are available. These comparisons confirm the viability and higher retrieval accuracies of the NN technique, when combined with the filtering constraints, for effective detection of KB HABs. Analysis of these results as well as sequential satellite observations and recent field measurements underline the importance of short-term temporal variabilities on retrieval accuracies.

The coordination- and photochemistry of copper(i) complexes: variation of N^N ligands from imidazole to tetrazole.

PubMed

Bergmann, Larissa; Braun, Carolin; Nieger, Martin; Bräse, Stefan

2018-01-02

The prediction of coordination modes is of high importance when structure-property relationships are discussed. Herein, the coordination chemistry of copper(i) with pyridine-amines with a varying number of coordinating N-atoms, namely pyridine-benzimidazole, -triazole and -tetrazole, or their deprotonated analogues, and different phosphines was systematically studied and the photoluminescence properties of all synthesized complexes examined and related to DFT data. Each complex was characterized by single-crystal X-ray analysis and elemental analysis, and a set of prediction rules derived for the coordination chemistry of copper(i) with these ligands. A mononuclear cationic coordination motif was found for PPh 3 or DPEPhos with all N^N ligands, which exhibits blue to green luminescence of MLCT character d(Cu) → π*(pyridine-amine ligand) with quantum yields up to 46%. With the deprotonated N^N ligands, mononuclear neutral complexes were only expected with DPEPhos. The emission's nature of this complex type is strongly dependent on the electronic effects of the N^N ligand and was characterized as (ML + IL)CT transition. In contrast to the high quantum yields up to 78% for the tetrazolate complexes (as reported before), the triazolate and imidazolate based complexes show much lower emission efficiencies below 10%. Besides the mononuclear copper(i) complexes, cluster-type complexes were obtained, which show moderate luminescence in the blue to green region of the visible spectrum (469-505 nm).

Previous exposure to musical auditory stimulation immediately influences the cardiac autonomic responses to the postural change maneuver in women.

PubMed

de Castro, Bianca Cr; Guida, Heraldo L; Roque, Adriano L; de Abreu, Luiz Carlos; Ferreira, Lucas L; Raimundo, Rodrigo D; Monteiro, Carlos Bm; Goulart, Flávia C; Ferreira, Celso; Marcomini, Renata S; Ribeiro, Vivian F; Ré, Alessandro Hn; Vanderlei, Luiz Carlos M; Valenti, Vitor E

2013-08-14

Chronic exposure to musical auditory stimulation has been reported to improve cardiac autonomic regulation. However, it is not clear if music acutely influences it in response to autonomic tests. We evaluated the acute effects of music on heart rate variability (HRV) responses to the postural change maneuver (PCM) in women. We evaluated 12 healthy women between 18 and 28 years old and HRV was analyzed in the time (SDNN, RMSSD, NN50 and pNN50) and frequency (LF, HF and LF/HF ratio) domains. In the control protocol, the women remained at seated rest for 10 minutes and quickly stood up within three seconds and remained standing still for 15 minutes. In the music protocol, the women remained at seated rest for 10 minutes, were exposed to music for 10 minutes and quickly stood up within three seconds and remained standing still for 15 minutes. HRV was recorded at the following time: rest, music (music protocol) 0-5, 5-10 and 10-15 min during standing. In the control protocol the SDNN, RMSSD and pNN50 indexes were reduced at 10-15 minutes after the volunteers stood up, while the LF (nu) index was increased at the same moment compared to seated rest. In the protocol with music, the indexes were not different from control but the RMSSD, pNN50 and LF (nu) were different from the music period. Musical auditory stimulation attenuates the cardiac autonomic responses to the PCM.

Reconstruction of K*+/-(892) in Au +Au Collisions at √sNN = 200 GeV

NASA Astrophysics Data System (ADS)

Zheng, He; STAR Collaboration

2016-09-01

The Relativistic Heavy Ion Collider (RHIC) produces a hot, dense and deconfined Quantum ChromoDynamics (QCD) medium, called the quark-gluon plasma (QGP), with Au +Au collisions at √sNN = 200 GeV. The K*+/-(892) resonance is a short-lived particle with a lifetime shorter than the expected lifetime of the QGP. The K* production may provide an effective tool to probe the QGP properties, such as strangeness enhancement. Experimentally, K*+/- analysis is difficult and less studied previously because of large combinatorial background. In recent years, improvements in data sample statistics and particle identification capability promise better K*+/- measurements. In this presentation, we report the reconstruction of K*+/- resonance via the hadronic decay channel K*+/- (892) ->KS0π+/- as a function of transverse momentum (pT) up to 5 GeV/c for various collision centrality classes. The data are Au +Au collisions at √sNN = 200 GeV collected in the year 2011 run from the STAR experiment. Physics implications of our measurements will also be discussed. For the STAR collaboration.

Effect of tunable redox behavior of bis chelate substituted 1,10-phenantroline Cu(II) complexes on its reaction with superoxide anion in DMSO. Toward a simple criterion to identify a SOD-like mechanism.

PubMed

Manzanera-Estrada, Mayra E; Cruz-Ramírez, Marisela; Flores-Alamo, Marcos; Gracia Y Jiménez, José Miguel; Galindo-Murillo, Rodrigo; García-Ramos, Juan Carlos; Ruiz-Azuara, Lena; Ortiz-Frade, Luis

2017-10-01

In this work we report a series of Cu(II) complexes [Cu(N-N) 2 (X)] + , (N-N=substituted 1,10-phenanthroline derivatives and X=Cl - or NO 3 - ), with tunable E 1/2 for electrochemical reduction [Cu II (N-N) 2 (X)] + +1e - ⇌[Cu I (N-N) 2 ]+X - . The disproportion of O 2 •- was explored in presence of the electro-generated species [Cu I (N-N) 2 ] + using cyclic voltammetry in a non-aqueous media, arising a new simple method to propose a SOD-like mechanism, which can be used as a quick guide test for a compound, before being proven in biological assays. It was found that complexes with high negative half wave potential values (E 1/2 ) for Cu(II)/Cu(I) couple shown a current increment for oxygen reduction, related to the capability of the disproportion of this reactive oxygen species. Copyright © 2017 Elsevier Inc. All rights reserved.

Augmented neural networks and problem structure-based heuristics for the bin-packing problem

NASA Astrophysics Data System (ADS)

Kasap, Nihat; Agarwal, Anurag

2012-08-01

In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.

N(+)-N and O(+)-O interaction energies, dipole transition moments, and transport cross sections

NASA Technical Reports Server (NTRS)

Partridge, H.; Stallcop, J. R.

1986-01-01

Complete sets of ion-atom interaction energies have been computed for nitrogen and oxygen with accurate large scale structure calculations. The computed energies agree well with the accurate potential curves available from spectroscopic measurement. The state functions from the nitrogen calculations have been applied to determine the transition moment for all allowed dipole transitions. These results can be combined to compute a detailed radiation spectrum such as that required to define the highly nonequilibrium environment of aeroassisted orbital transfer vehicle (AOTV). The long-range interaction energies have been used to determine the ion-atom resonance charge exchange cross sections that are important for transport processes such as diffusion. A calculation to determine reliable transport properties for energies that include the AOTV temperature range from these computed properties is described.

Adiabatic and coupled channels calculations for near barrier fusion of 16O +238U using realistic nucleon-nucleon interaction

NASA Astrophysics Data System (ADS)

Ismail, M.; Seif, W. M.; Botros, M. M.

2016-04-01

We investigate the fusion cross-section and the fusion barrier distribution of 16O +238U at near- and sub-barrier energies. We use an interaction potential generated by the semi-microscopic double folding model-based on density dependent (DD) form of the realistic Michigan-three-Yukawa (M3Y) Reid nucleon-nucleon (NN) interaction. We studied the role of both the static and dynamic deformations of the target nucleus on the fusion process. Rotational and vibrational degrees of freedom of 238U-nucleus are considered. We found that the deformation and the octupole vibrations in 238U enhance its sub-barrier fusion cross-section. The signature of the the octupole vibrational modes of 238U appears clearly in its fusion barrier distribution profile.

Measurement of undisturbed di-nitrogen emissions from aquatic ecosystems

NASA Astrophysics Data System (ADS)

Qin, Shuping, Clough, Timothy, Lou, Jiafa; Hu, Chunsheng; Oenema, Oene; Wrage-Mönnig, Nicole; Zhang, Yuming

2016-04-01

Increased production of reactive nitrogen (Nr) from atmospheric di-nitrogen (N2) during the last century has greatly contributed to increased food production1-4. However, enriching the biosphere with Nr through N fertilizer production, combustion, and biological N2 fixation has also caused a series of negative effects on global ecosystems 5,6, especially aquatic ecosystems7. The main pathway converting Nr back into the atmospheric N2 pool is the last step of the denitrification process, i.e., the reduction of nitrous oxide (N2O) into N2 by micro-organisms7,8. Despite several attempts9,10, there is not yet an accurate, fast and direct method for measuring undisturbed N2 fluxes from denitrification in aquatic sediments at the field scale11-14. Such a method is essential to study the feedback of aquatic ecosystems to Nr inputs1,2,7. Here we show that the measurement of both N2O emission and its isotope signature can be used to infer the undisturbed N2 fluxes from aquatic ecosystems. The microbial reduction of N2O increases the natural abundance of 15N-N2O relative to 14N-N2O (δ15N-N2O). We observed linear relationships between δ15N-N2O and the logarithmic transformed N2O/(N2+N2O) emission ratios. Through independent measurements, we verified that the undisturbed N2 flux from aquatic ecosystems can be inferred from measurements of N2O emissions and the δ15N-N2O signature. Our method allows the determination of field-scale N2 fluxes from undisturbed aquatic ecosystems, and thereby allows model predictions of denitrification rates to be tested. The undisturbed N2 fluxes observed are almost one order of magnitude higher than those estimated by the traditional method, where perturbation of the system occurs, indicating that the ability of aquatic ecosystems to remove Nr may have been severely underestimated.

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

Wagner, Albert F., E-mail: wagner@anl.gov; Dawes, Richard; Continetti, Robert E.

The measured H(D)OCO survival fractions of the photoelectron-photofragment coincidence experiments by the Continetti group are qualitatively reproduced by tunneling calculations to H(D) + CO{sub 2} on several recent ab initio potential energy surfaces for the HOCO system. The tunneling calculations involve effective one-dimensional barriers based on steepest descent paths computed on each potential energy surface. The resulting tunneling probabilities are converted into H(D)OCO survival fractions using a model developed by the Continetti group in which every oscillation of the H(D)-OCO stretch provides an opportunity to tunnel. Four different potential energy surfaces are examined with the best qualitative agreement with experimentmore » occurring for the PIP-NN surface based on UCCSD(T)-F12a/AVTZ electronic structure calculations and also a partial surface constructed for this study based on CASPT2/AVDZ electronic structure calculations. These two surfaces differ in barrier height by 1.6 kcal/mol but when matched at the saddle point have an almost identical shape along their reaction paths. The PIP surface is a less accurate fit to a smaller ab initio data set than that used for PIP-NN and its computed survival fractions are somewhat inferior to PIP-NN. The LTSH potential energy surface is the oldest surface examined and is qualitatively incompatible with experiment. This surface also has a small discontinuity that is easily repaired. On each surface, four different approximate tunneling methods are compared but only the small curvature tunneling method and the improved semiclassical transition state method produce useful results on all four surfaces. The results of these two methods are generally comparable and in qualitative agreement with experiment on the PIP-NN and CASPT2 surfaces. The original semiclassical transition state theory method produces qualitatively incorrect tunneling probabilities on all surfaces except the PIP. The Eckart tunneling method uses the least amount of information about the reaction path and produces too high a tunneling probability on PIP-NN surface, leading to survival fractions that peak at half their measured values.« less

A 10 nN resolution thrust-stand for micro-propulsion devices

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

Chakraborty, Subha; Courtney, Daniel G.; Shea, Herbert, E-mail: herbert.shea@epfl.ch

We report on the development of a nano-Newton thrust-stand that can measure up to 100 μN thrust from different types of microthrusters with 10 nN resolution. The compact thrust-stand measures the impingement force of the particles emitted from a microthruster onto a suspended plate of size 45 mm × 45 mm and with a natural frequency over 50 Hz. Using a homodyne (lock-in) readout provides strong immunity to facility vibrations, which historically has been a major challenge for nano-Newton thrust-stands. A cold-gas thruster generating up to 50 μN thrust in air was first used to validate the thrust-stand. Better thanmore » 10 nN resolution and a minimum detectable thrust of 10 nN were achieved. Thrust from a miniature electrospray propulsion system generating up to 3 μN of thrust was measured with our thrust-stand in vacuum, and the thrust was compared with that computed from beam diagnostics, obtaining agreement within 50 nN to 150 nN. The 10 nN resolution obtained from this thrust-stand matches that from state-of-the-art nano-Newton thrust-stands, which measure thrust directly from the thruster by mounting it on a moving arm (but whose natural frequency is well below 1 Hz). The thrust-stand is the first of its kind to demonstrate less than 3 μN resolution by measuring the impingement force, making it capable of measuring thrust from different types of microthrusters, with the potential of easy upscaling for thrust measurement at much higher levels, simply by replacing the force sensor with other force sensors.« less

Material discovery by combining stochastic surface walking global optimization with a neural network.

PubMed

Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-09-01

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

A 10 nN resolution thrust-stand for micro-propulsion devices

NASA Astrophysics Data System (ADS)

Chakraborty, Subha; Courtney, Daniel G.; Shea, Herbert

2015-11-01

We report on the development of a nano-Newton thrust-stand that can measure up to 100 μN thrust from different types of microthrusters with 10 nN resolution. The compact thrust-stand measures the impingement force of the particles emitted from a microthruster onto a suspended plate of size 45 mm × 45 mm and with a natural frequency over 50 Hz. Using a homodyne (lock-in) readout provides strong immunity to facility vibrations, which historically has been a major challenge for nano-Newton thrust-stands. A cold-gas thruster generating up to 50 μN thrust in air was first used to validate the thrust-stand. Better than 10 nN resolution and a minimum detectable thrust of 10 nN were achieved. Thrust from a miniature electrospray propulsion system generating up to 3 μN of thrust was measured with our thrust-stand in vacuum, and the thrust was compared with that computed from beam diagnostics, obtaining agreement within 50 nN to 150 nN. The 10 nN resolution obtained from this thrust-stand matches that from state-of-the-art nano-Newton thrust-stands, which measure thrust directly from the thruster by mounting it on a moving arm (but whose natural frequency is well below 1 Hz). The thrust-stand is the first of its kind to demonstrate less than 3 μN resolution by measuring the impingement force, making it capable of measuring thrust from different types of microthrusters, with the potential of easy upscaling for thrust measurement at much higher levels, simply by replacing the force sensor with other force sensors.

One-dimensional pion, kaon, and proton femtoscopy in Pb-Pb collisions at s NN = 2.76 TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2015-11-19

Tmore » he size of the particle emission region in high-energy collisions can be deduced using the femtoscopic correlations of particle pairs at low relative momentum. Such correlations arise due to quantum statistics and Coulomb and strong final state interactions. In this paper, results are presented from femtoscopic analyses of π ± π ±, K ± K ±, K$$0\\atop{S}$$K$$0\\atop{S}$$, pp , and $$\\overline{p}$$ $$\\overline{p}$$ correlations from Pb-Pb collisions at s NN = 2.76 eV by the ALICE experiment at the LHC. One-dimensional radii of the system are extracted from correlation functions in terms of the invariant momentum difference of the pair. he comparison of the measured radii with the predictions from a hydrokinetic model is discussed. he pion and kaon source radii display a monotonic decrease with increasing average pair transverse mass m which is consistent with hydrodynamic model predictions for central collisions. Lastly, the kaon and proton source sizes can be reasonably described by approximate m scaling.« less

One-dimensional pion, kaon, and proton femtoscopy in Pb-Pb collisions at √{sNN}=2.76 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aimo, I.; Aiola, S.; Ajaz, M.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Armesto, N.; Arnaldi, R.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Bach, M.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Baltasar Dos Santos Pedrosa, F.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blanco, F.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botje, M.; Botta, E.; Böttger, S.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Cavicchioli, C.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Chunhui, Z.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa Del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; de, S.; de Caro, A.; de Cataldo, G.; de Cuveland, J.; de Falco, A.; de Gruttola, D.; De Marco, N.; de Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; D'Erasmo, G.; di Bari, D.; di Mauro, A.; di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Dobrowolski, T.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Engel, H.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Eschweiler, D.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Felea, D.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Gomez Ramirez, A.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Grosse-Oetringhaus, J. F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gulkanyan, H.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hansen, A.; Harris, J. W.; Hartmann, H.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hilden, T. E.; Hillemanns, H.; Hippolyte, B.; Hosokawa, R.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Ionita, C.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacobs, P. M.; Jadlovska, S.; Jahnke, C.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, K. H.; Khan, M. M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, B.; Kim, D. W.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobayashi, T.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Kox, S.; Koyithatta Meethaleveedu, G.; Kral, J.; Králik, I.; Kravčáková, A.; Krelina, M.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kugathasan, T.; Kuhn, C.; Kuijer, P. G.; Kulakov, I.; Kumar, J.; Kumar, L.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Legrand, I.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loggins, V. R.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Luz, P. H. F. N. D.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manceau, L.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Martynov, Y.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Masui, H.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; McDonald, D.; Meddi, F.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Morando, M.; Moreira de Godoy, D. A.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Murray, S.; Musa, L.; Musinsky, J.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pajares, C.; Pal, S. K.; Pan, J.; Pandey, A. K.; Pant, D.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Peitzmann, T.; Pereira da Costa, H.; Pereira de Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Real, J. S.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rivetti, A.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salgado, C. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sanchez Castro, X.; Šándor, L.; Sandoval, A.; Sano, M.; Santagati, G.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Seeder, K. S.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Seo, J.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Soltz, R.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Sultanov, R.; Šumbera, M.; Symons, T. J. M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Takahashi, J.; Tanaka, N.; Tangaro, M. A.; Tapia Takaki, J. D.; Tarantola Peloni, A.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; van der Maarel, J.; van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Wang, Y.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Wessels, J. P.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.; Alice Collaboration

2015-11-01

The size of the particle emission region in high-energy collisions can be deduced using the femtoscopic correlations of particle pairs at low relative momentum. Such correlations arise due to quantum statistics and Coulomb and strong final state interactions. In this paper, results are presented from femtoscopic analyses of π±π±,K±K±,KS0KS0,p p , and p ¯p ¯ correlations from Pb-Pb collisions at √{sNN}=2.76 TeV by the ALICE experiment at the LHC. One-dimensional radii of the system are extracted from correlation functions in terms of the invariant momentum difference of the pair. The comparison of the measured radii with the predictions from a hydrokinetic model is discussed. The pion and kaon source radii display a monotonic decrease with increasing average pair transverse mass mT which is consistent with hydrodynamic model predictions for central collisions. The kaon and proton source sizes can be reasonably described by approximate mT scaling.

Dredging Operations Technical Support Program. Mathematical Model of the Consolidation/Desiccation Processes in Dredged Material.

DTIC Science & Technology

1985-07-01

5695 BF(I) - BETA(LDF) * GOTO 7 5700 6 NN - N-i 5705 Ch - CI / (ES(N)-ES(NN)) 5710 AF(I) w ALPHA(N) + CM*(ALPHA(N)-ALPHA(NN)) 5715 DF(I) a BETA(N) 4...1OXP34t4SETTLEMENT DUE TO CONSOLIDATION - PF1O.4) 7845 113 FORMAT(/1OX932HSETTLEMENT DUE TO DESICCATION - PF1O.4) 7850 114 FORNAT(/1OX920HSURFACE

Electromagnetic Induction Spectroscopy for the Detection of Subsurface Targets

DTIC Science & Technology

2012-12-01

curves of the proposed method and that of Fails et al.. For the kNN ROC curve, k = 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81...et al. [6] and Ramachandran et al. [7] both demonstrated success in detecting mines using the k-nearest-neighbor ( kNN ) algorithm based on the EMI...error is also included in the feature vector. The kNN labels an unknown target based on the closest targets in a training set. Collins et al. [2] and

PyNN: A Common Interface for Neuronal Network Simulators.

PubMed

Davison, Andrew P; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

2008-01-01

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.

The National Network of Libraries of Medicine's outreach to the public health workforce: 2001–2006

PubMed Central

Cogdill, Keith W.; Ruffin, Angela B.; Stavri, P. Zoë

2007-01-01

Objective: The paper provides an overview of the National Network of Libraries of Medicine's (NN/ LM's) outreach to the public health workforce from 2001 to 2006. Description: NN/LM conducts outreach through the activities of the Regional Medical Library (RML) staff and RML-sponsored projects led by NN/LM members. Between 2001 and 2006, RML staff provided training on information resources and information management for public health personnel at national, state, and local levels. The RMLs also contributed significantly to the Partners in Information Access for the Public Health Workforce collaboration. Methods: Data were extracted from telephone interviews with directors of thirty-seven NN/LM-sponsored outreach projects directed at the public health sector. A review of project reports informed the interviews, which were transcribed and subsequently coded for emergent themes using qualitative analysis software. Results: Analysis of interview data led to the identification of four major themes: training, collaboration, evaluation of outcomes, and challenges. Sixteen subthemes represented specific lessons learned from NN/LM members' outreach to the public health sector. Conclusions: NN/LM conducted extensive information-oriented outreach to the public health workforce during the 2001-to-2006 contract period. Lessons learned from this experience, most notably the value of collaboration and the need for flexibility, continue to influence outreach efforts in the current contract period. PMID:17641766

On prognostic models, artificial intelligence and censored observations.

PubMed

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

PubMed

Mall, Susmita; Chakraverty, S

2016-08-01

Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

PyNN: A Common Interface for Neuronal Network Simulators

PubMed Central

Davison, Andrew P.; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

2008-01-01

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

LEAP 1992: Conference summary

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

Dover, C.B.

1992-12-01

We present a summary of the many new results in antiproton ({bar p}) physics presented at the LEAP `92 conference, in the areas of meson spectroscopy, {bar N}N scattering, annihilation and spin observables, strangeness and charm production, {bar N} annihilation in nuclei, atomic physics with very low energy {bar p}`s, the exploration of fundamental symmetries and interactions with {bar p} (CP, T, CPT, gravitation), and the prospects for new {bar p} facilities at ultralow energies or energies above the LEAR regime ({ge} 2 GeV/c).

LEAP 1992: Conference summary

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

Dover, C.B.

1992-12-01

We present a summary of the many new results in antiproton ([bar p]) physics presented at the LEAP '92 conference, in the areas of meson spectroscopy, [bar N]N scattering, annihilation and spin observables, strangeness and charm production, [bar N] annihilation in nuclei, atomic physics with very low energy [bar p]'s, the exploration of fundamental symmetries and interactions with [bar p] (CP, T, CPT, gravitation), and the prospects for new [bar p] facilities at ultralow energies or energies above the LEAR regime ([ge] 2 GeV/c).

Initial Eccentricity in Deformed 197Au+197Au and 238U+238U Collisions at RHIC

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

Filip, Peter; Lednicky, Richard; Masui, Hiroshi

2010-07-07

Initial eccentricity and eccentricity fluctuations of the interaction volume created in relativistic collisions of deformed {sup 197}Au and {sup 238}U nuclei are studied using optical and Monte-Carlo (MC) Glauber simulations. It is found that the non-sphericity noticeably influences the average eccentricity in central collisions and eccentricity fluctuations are enhanced due to deformation. Quantitative results are obtained for Au+Au and U+U collisions at energy {radical}s{sub NN} = 200 GeV.

Differential regulation of gene expression of neurotensin and prohormone convertases PC1 and PC2 in the bovine ocular ciliary epithelium: possible implications on neurotensin processing.

PubMed

Ortego, Javier; Wollmann, Guido; Coca-Prados, Miguel

2002-11-15

Prohormone convertases PC1 and PC2 are enzymes involved in the intracellular processing of pro-neurotensin/neuromedin N (pro-NT/NN) through the regulated secretory pathway. In this study, we present evidence of the differential gene expression of pro-NT/NN, pro-PC1 and pro-PC2 in two cell lines established from the neuroendocrine ocular ciliary epithelium. Dexamethasone and forskolin were found to synergistically up-regulate NT/NN mRNA expression in both cell types. The pigmented cells released NT, and this release was enhanced by agents that induced its biosynthesis. In contrast, nonpigmented cells exhibited a significantly reduced neurotensin secretion in response to inducers, leading to an accumulation of the peptide. PC1 and PC2 mRNA expression was induced in a cell-specific manner by the same agents that enhanced pro-NT/NN biosynthesis. These results demonstrate cell-specific processing of pro-NT/NN by the ciliary epithelium. Copyright 2002 Elsevier Science Ireland Ltd.

Inactivation of neurotensin and neuromedin N by Zn metallopeptidases.

PubMed

Kitabgi, Patrick

2006-10-01

The two related peptides neurotensin (NT) and neuromedin N (NN) are efficiently inactivated by peptidases in vitro. Whereas NT is primarily degraded by a combination of three Zn metallo-endopeptidases, namely endopeptidases 24.11, 24.15 and 24.16, in all systems examined, NN is essentially inactivated by the Zn metallo-exopeptidase aminopeptidase M. In this paper we review the work that has led to the identification of the NT- and NN-degrading enzymes and to the purification and cloning of EP 24.16, a previously unidentified peptidase. We provide a brief description of the three NT-inactivating endopeptidases and of their specific and mixed inhibitors, some of them developed in the course of studying NT degradation. Finally, we review in vivo data obtained with these inhibitors that strongly support a physiological role for EP 24.11, 24.15 and 24.16 in the termination of NT-generated signals and for aminopeptidase in terminating NN action. Knowledge of the NT and NN inactivation mechanisms offers the perspective to develop metabolically stable analogs of these peptides with potential therapeutic value.

Control of Complex Dynamic Systems by Neural Networks

NASA Technical Reports Server (NTRS)

Spall, James C.; Cristion, John A.

1993-01-01

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Comparison of Sleep Disorders between Real and Simulated 3,450-m Altitude

PubMed Central

Heinzer, Raphaël; Saugy, Jonas J.; Rupp, Thomas; Tobback, Nadia; Faiss, Raphael; Bourdillon, Nicolas; Rubio, José Haba; Millet, Grégoire P.

2016-01-01

Study Objectives: Hypoxia is known to generate sleep-disordered breathing but there is a debate about the pathophysiological responses to two different types of hypoxic exposure: normobaric hypoxia (NH) and hypobaric hypoxia (HH), which have never been directly compared. Our aim was to compare sleep disorders induced by these two types of altitude. Methods: Subjects were exposed to 26 h of simulated (NH) or real altitude (HH) corresponding to 3,450 m and a control condition (NN) in a randomized order. The sleep assessments were performed with nocturnal polysomnography (PSG) and questionnaires. Thirteen healthy trained males subjects volunteered for this study (mean ± SD; age 34 ± 9 y, body weight 76.2 ± 6.8 kg, height 179.7 ± 4.2 cm). Results: Mean nocturnal oxygen saturation was further decreased during HH than in NH (81.2 ± 3.1 versus 83.6 ± 1.9%; P < 0.01) when compared to NN (95.5 ± 0.9%; P < 0.001). Heart rate was higher in HH than in NH (61 ± 10 versus 55 ± 6 bpm; P < 0.05) and NN (48 ± 5 bpm; P < 0.001). Total sleep time was longer in HH than in NH (351 ± 63 versus 317 ± 65 min, P < 0.05), and both were shorter compared to NN (388 ± 50 min, P < 0.05). Breathing frequency did not differ between conditions. Apnea-hypopnea index was higher in HH than in NH (20.5 [15.8–57.4] versus 11.4 [5.0–65.4]; P < 0.01) and NN (8.2 [3.9–8.8]; P < 0.001). Subjective sleep quality was similar between hypoxic conditions but lower than in NN. Conclusions: Our results suggest that HH has a greater effect on nocturnal breathing and sleep structure than NH. In HH, we observed more periodic breathing, which might arise from the lower saturation due to hypobaria, but needs to be confirmed. Citation: Heinzer R, Saugy JJ, Rupp T, Tobback N, Faiss R, Bourdillon N, Rubio JH, Millet GP. Comparison of sleep disorders between real and simulated 3,450-m altitude. SLEEP 2016;39(8):1517–1523. PMID:27166242

Determination of the Interaction Position of Gamma Photons in Monolithic Scintillators Using Neural Network Fitting

NASA Astrophysics Data System (ADS)

Conde, P.; Iborra, A.; González, A. J.; Hernández, L.; Bellido, P.; Moliner, L.; Rigla, J. P.; Rodríguez-Álvarez, M. J.; Sánchez, F.; Seimetz, M.; Soriano, A.; Vidal, L. F.; Benlloch, J. M.

2016-02-01

In Positron Emission Tomography (PET) detectors based on monolithic scintillators, the photon interaction position needs to be estimated from the light distribution (LD) on the photodetector pixels. Due to the finite size of the scintillator volume, the symmetry of the LD is truncated everywhere except for the crystal center. This effect produces a poor estimation of the interaction positions towards the edges, an especially critical situation when linear algorithms, such as Center of Gravity (CoG), are used. When all the crystal faces are painted black, except the one in contact with the photodetector, the LD can be assumed to behave as the inverse square law, providing a simple theoretical model. Using this LD model, the interaction coordinates can be determined by means of fitting each event to a theoretical distribution. In that sense, the use of neural networks (NNs) has been shown to be an effective alternative to more traditional fitting techniques as nonlinear least squares (LS). The multilayer perceptron is one type of NN which can model non-linear functions well and can be trained to accurately generalize when presented with new data. In this work we have shown the capability of NNs to approximate the LD and provide the interaction coordinates of γ-photons with two different photodetector setups. One experimental setup was based on analog Silicon Photomultipliers (SiPMs) and a charge division diode network, whereas the second setup was based on digital SiPMs (dSiPMs). In both experiments NNs minimized border effects. Average spatial resolutions of 1.9 ±0.2 mm and 1.7 ±0.2 mm for the entire crystal surface were obtained for the analog and dSiPMs approaches, respectively.

Evaluation of adhesion forces of Staphylococcus aureus along the length of Candida albicans hyphae.

PubMed

Ovchinnikova, Ekaterina S; Krom, Bastiaan P; Busscher, Henk J; van der Mei, Henny C

2012-11-27

Candida albicans is a human fungal pathogen, able to cause both superficial and serious, systemic diseases and is able to switch from yeast cells to long, tube-like hyphae, depending on the prevailing environmental conditions. Both morphological forms of C. albicans are found in infected tissue, often in combination with Staphylococcus aureus. Although bacterial adhesion to the different morphologies of C. albicans has been amply studied, possible differences in staphylococcal adhesion forces along the length of C. albicans hyphae have never been determined. In this study, we aim to verify the hypothesis that the forces mediating S. aureus NCTC8325-4GFP adhesion to hyphae vary along the length of C. albicans SC5314 and MB1 hyphae, as compared with adhesion to yeast cells. C. albicans hyphae were virtually divided into a "tip" (the growing and therefore youngest part of the hyphae), a "middle" and a so-called "head" region (the yeast cell from which germination started). Adhesion forces between S. aureus NCTC8325-4GFP and the different regions of C. albicans SC5314 hyphae were measured using atomic force microscopy. Strong adhesion forces were found at the tip and middle regions of C. albicans hyphae (-4.1 nN and -4.0 nN, respectively), while much smaller adhesion forces were measured at the head region (-0.3 nN). Adhesion forces exerted by the head region were comparable with the forces arising from budding yeast cells (-0.5 nN). A similar regional dependence of the staphylococcal adhesion forces was found for the clinical isolate involved in this study, C. albicans MB1. This is the first time that differences in adhesion forces between S. aureus and different regions of C. albicans hyphae have been demonstrated on a quantitative basis, supporting the view that the head region is different from the remainder of the hyphae. Notably it can be concluded that the properties of the hyphal head region are similar to those of budding yeast cells. These novel findings provide new insights in the intricate interkingdom interaction between C. albicans and S. aureus.

Nearest Neighbor Algorithms for Pattern Classification

NASA Technical Reports Server (NTRS)

Barrios, J. O.

1972-01-01

A solution of the discrimination problem is considered by means of the minimum distance classifier, commonly referred to as the nearest neighbor (NN) rule. The NN rule is nonparametric, or distribution free, in the sense that it does not depend on any assumptions about the underlying statistics for its application. The k-NN rule is a procedure that assigns an observation vector z to a category F if most of the k nearby observations x sub i are elements of F. The condensed nearest neighbor (CNN) rule may be used to reduce the size of the training set required categorize The Bayes risk serves merely as a reference-the limit of excellence beyond which it is not possible to go. The NN rule is bounded below by the Bayes risk and above by twice the Bayes risk.

Permutation entropy analysis of financial time series based on Hill's diversity number

NASA Astrophysics Data System (ADS)

Zhang, Yali; Shang, Pengjian

2017-12-01

In this paper the permutation entropy based on Hill's diversity number (Nn,r) is introduced as a new way to assess the complexity of a complex dynamical system such as stock market. We test the performance of this method with simulated data. Results show that Nn,r with appropriate parameters is more sensitive to the change of system and describes the trends of complex systems clearly. In addition, we research the stock closing price series from different data that consist of six indices: three US stock indices and three Chinese stock indices during different periods, Nn,r can quantify the changes of complexity for stock market data. Moreover, we get richer information from Nn,r, and obtain some properties about the differences between the US and Chinese stock indices.

Economic Effectiveness of Mandatory Engine Maintenance for ...

EPA Pesticide Factsheets

... SOCIAL IIIIII~~II~'IIII~~II ::¥~~NN~~~~~G MODEl ... failing an inspec- tion based upon a rejection criteria for each maladjustment are then sent ...

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at s NN = 2.76 TeV

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

Adam, J.; Adamová, D.; Aggarwal, M. M.

Here, we report on results obtained with the event-shape engineering technique applied to Pb-Pb collisions at root √s NN = 2.76 TeV. By selecting events in the same centrality interval, but with very different average flow, different initial-state conditions can be studied. We find the effect of the event-shape selection on the elliptic flow coefficient v 2 to be almost independent of transverse momentum p T, which is as expected if this effect is attributable to fluctuations in the initial geometry of the system. Charged-hadron, -pion, -kaon, and -proton transverse momentum distributions are found to be harder in events withmore » higher-than-average elliptic flow, indicating an interplay between radial and elliptic flow.« less

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at s NN = 2.76 TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2016-03-31

Here, we report on results obtained with the event-shape engineering technique applied to Pb-Pb collisions at root √s NN = 2.76 TeV. By selecting events in the same centrality interval, but with very different average flow, different initial-state conditions can be studied. We find the effect of the event-shape selection on the elliptic flow coefficient v 2 to be almost independent of transverse momentum p T, which is as expected if this effect is attributable to fluctuations in the initial geometry of the system. Charged-hadron, -pion, -kaon, and -proton transverse momentum distributions are found to be harder in events withmore » higher-than-average elliptic flow, indicating an interplay between radial and elliptic flow.« less

Flavors in the soup: An overview of heavy-flavored jet energy loss at CMS

NASA Astrophysics Data System (ADS)

Jung, Kurt E.

The energy loss of jets in heavy-ion collisions is expected to depend on the flavor of the fragmenting parton. Thus, measurements of jet quenching as a function of flavor place powerful constraints on the thermodynamical and transport properties of the hot and dense medium. Measurements of the nuclear modification factors of the heavy flavor tagged jets from charm and bottom quarks in both PbPb and pPb collisions can quantify such energy loss effects. Specifically, pPb measurements provide crucial insights into the behavior of the cold nuclear matter effect, which is required to fully understand the hot and dense medium effects on jets in PbPb collisions. This dissertation presents the energy modification of b-jets in PbPb at √sNN = 2.76 TeV and pPb collisions at √sNN = 5.02 TeV, along with the first ever measurements of charm jets in pPb collisions at √s NN =5.02 TeV and in pp collisions at √s = 2.76 TeV. Measurements of b-jet and c-jet spectra are compared to pp data at √s = 2.76 TeV and to PYTHIA predictions at both 2.76 and 5.02 TeV. We observe a centrality-dependent suppression for b-jets in PbPb and a result that is consistent with PYTHIA for both charm and bottom jets in pPb collisions.

Short-term estimation of GNSS TEC using a neural network model in Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Arthur Amaral; Borges, Renato Alves; Paparini, Claudia; Ciraolo, Luigi; Radicella, Sandro M.

2017-10-01

This work presents a novel Neural Network (NN) model to estimate Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) measurements in three distinct sectors in Brazil. The purpose of this work is to start the investigations on the development of a regional model that can be used to determine the vertical TEC over Brazil, aiming future applications on a near real-time frame estimations and short-term forecasting. The NN is used to estimate the GNSS TEC values at void locations, where no dual-frequency GNSS receiver that may be used as a source of data to GNSS TEC estimation is available. This approach is particularly useful for GNSS single-frequency users that rely on corrections of ionospheric range errors by TEC models. GNSS data from the first GLONASS network for research and development (GLONASS R&D network) installed in Latin America, and from the Brazilian Network for Continuous Monitoring of the GNSS (RMBC) were used on TEC calibration. The input parameters of the NN model are based on features known to influence TEC values, such as geographic location of the GNSS receiver, magnetic activity, seasonal and diurnal variations, and solar activity. Data from two ten-days periods (from DoY 154 to 163 and from 282 to 291) are used to train the network. Three distinct analyses have been carried out in order to assess time-varying and spatial performance of the model. At the spatial performance analysis, for each region, a set of stations is chosen to provide training data to the NN, and after the training procedure, the NN is used to estimate vTEC behavior for the test station which data were not presented to the NN in training process. An analysis is done by comparing, for each testing station, the estimated NN vTEC delivered by the NN and reference calibrated vTEC. Also, as a second analysis, the network ability to forecast one day after the time interval (DoY 292) based on information of the second period of investigation is also assessed in order to verify the feasibility on using low amount of data for short-term forecasting. In a third analysis, the spatial performance of the NN model is assessed and compared against CODE Global Ionospheric Maps during the geomagnetic storm registered on 13th and 14th October 2016. The results obtained from the three described analyses indicate that even using a ten-days period of data to train the network, the proposed NN model provides good spatial performance and presents to be a promising tool for short-term forecasting. The results obtained in the analysis presented a root mean squared error less than 7.9 TECU in all scenarios under investigation.

The isotropic-nematic and nematic-nematic phase transition of binary mixtures of tangent hard-sphere chain fluids: An analytical equation of state

NASA Astrophysics Data System (ADS)

van Westen, Thijs; Vlugt, Thijs J. H.; Gross, Joachim

2014-01-01

An analytical equation of state (EoS) is derived to describe the isotropic (I) and nematic (N) phase of linear- and partially flexible tangent hard-sphere chain fluids and their mixtures. The EoS is based on an extension of Onsager's second virial theory that was developed in our previous work [T. van Westen, B. Oyarzún, T. J. H. Vlugt, and J. Gross, J. Chem. Phys. 139, 034505 (2013)]. Higher virial coefficients are calculated using a Vega-Lago rescaling procedure, which is hereby generalized to mixtures. The EoS is used to study (1) the effect of length bidispersity on the I-N and N-N phase behavior of binary linear tangent hard-sphere chain fluid mixtures, (2) the effect of partial molecular flexibility on the binary phase diagram, and (3) the solubility of hard-sphere solutes in I- and N tangent hard-sphere chain fluids. By changing the length bidispersity, two types of phase diagrams were found. The first type is characterized by an I-N region at low pressure and a N-N demixed region at higher pressure that starts from an I-N-N triphase equilibrium. The second type does not show the I-N-N equilibrium. Instead, the N-N region starts from a lower critical point at a pressure above the I-N region. The results for the I-N region are in excellent agreement with the results from molecular simulations. It is shown that the N-N demixing is driven both by orientational and configurational/excluded volume entropy. By making the chains partially flexible, it is shown that the driving force resulting from the configurational entropy is reduced (due to a less anisotropic pair-excluded volume), resulting in a shift of the N-N demixed region to higher pressure. Compared to linear chains, no topological differences in the phase diagram were found. We show that the solubility of hard-sphere solutes decreases across the I-N phase transition. Furthermore, it is shown that by using a liquid crystal mixture as the solvent, the solubility difference can by maximized by tuning the composition. Theoretical results for the Henry's law constant of the hard-sphere solute are in good agreement with the results from molecular simulation.

Effects of Orthostatism and Hemodialysis on Mean Heart Period and Fractal Heart Rate Properties of Chronic Renal Failure Patients.

PubMed

Echeverría, Juan C; Infante, Oscar; Pérez-Grovas, Héctor; González, Hortensia; José, Marco V; Lerma, Claudia

2017-11-01

The aim of this work was to evaluate the short-term fractal index (α 1 ) of heart rate variability (HRV) in chronic renal failure (CRF) patients by identifying the effects of orthostatism and hemodialysis (HD), and by evaluating the correlation between α 1 and the mean RR interval from sinus beats (meanNN). HRV time series were derived from ECG data of 19 CRF patients and 20 age-matched healthy subjects obtained at supine and orthostatic positions (lasting 5 min each). Data from CRF patients were collected before and after HD. α 1 was calculated from each time series and compared by analysis of variance. Pearson's correlations between meanNN and α 1 were calculated using the data from both positions by considering three groups: healthy subjects, CRF before HD and CRF after HD. At supine position, α 1 of CRF patients after HD (1.17 ± 0.30) was larger (P < 0.05) than in healthy subjects (0.89 ± 0.28) but not before HD (1.10 ± 0.34). α 1 increased (P < 0.05) in response to orthostatism in healthy subjects (1.29 ± 0.26) and CRF patients after HD (1.34 ± 0.31), but not before HD (1.25 ± 0.37). Whereas α 1 was correlated (P < 0.05) with the meanNN of healthy subjects (r = -0.562) and CRF patients after HD (r = -0.388), no significance in CRF patients before HD was identified (r = 0.003). Multiple regression analysis confirmed that α 1 was mainly predicted by the orthostatic position (in all groups) and meanNN (healthy subjects and patients after HD), showing no association with the renal disease condition in itself. In conclusion, as in healthy subjects, α 1 of CRF patients correlates with meanNN after HD (indicating a more irregular-like HRV behavior at slower heart rates). This suggests that CRF patients with stable blood pressure preserve a regulatory adaptability despite a shifted setting point of the heart period (i.e., higher heart rate) in comparison with healthy subjects. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Psychophysiological Effects of Aging - Developing a Functional Age Index for Pilots. II Taxonomy of Psychological Factors

DTIC Science & Technology

1978-04-01

84 MieWJede apsed orI0 FoWap., ash.. 12’ Ii’ lase. sm. Is" 14 viand seas v7 3 IS Selected woog acurse U1Ds eia li of satia. SiP 215’ Fmxbd2 I 54 Dely...considered as preliminary by the authors (44). 32 I rnnvBESI AVAIISA LE LLWI *.4U’ -I C 2!2d 1 - N I2Ak -...4W O. SR N NN4 .-. N - N NN...South Africa, October 24-29, 1976. 91. Verra, G., 3. Martin, 3. P. Crance, and N. Boulange: La Motivation chez les Pilotes d’Aviation Legere I

Stretching of short monatomic gold chains-some model calculations

NASA Astrophysics Data System (ADS)

Sumali, Priyanka, Verma, Veena; Dharamvir, Keya

2012-06-01

The Mechanical properties of zig-zag monatomic gold chains containing 5 and 7 atoms were studied using the Siesta Code (SC), which works within the framework of DFT formalism and Gupta Potential (GP), which is an effective atom-atom potential. The zig-zag chains were stretched by keeping the end atoms fixed while rest of the atoms were relaxed till minimum energy is obtained. Energy, Force and Young's Modulus found using GP and SC were plotted as functions of total length. It is found that the breaking force in case of GP is of order of 1.6nN while for SIESTA is of the order of 2.9nN for both the chains.

First principles study of neutral and anionic (medium-size) aluminum nitride clusters: AlnNn, n=7-16.

PubMed

Costales, Aurora; Blanco, M A; Francisco, E; Pendas, A Martín; Pandey, Ravindra

2006-03-09

We report the results of a theoretical study of AlnNn (n=7-16) clusters that is based on density functional theory. We will focus on the evolution of structural and electronic properties with the cluster size in the stoichiometric AlN clusters considered. The results reveal that the structural and electronic properties tend to evolve toward their respective bulk limits. The rate of evolution is, however, slow due to the hollow globular shape exhibited by the clusters, which introduces large surface effects that dominate the properties studied. We will also discuss the changes induced upon addition of an extra electron to the respective neutral clusters.

Highly potent growth hormone secretagogues: hybrids of NN703 and ipamorelin.

PubMed

Hansen, T K; Ankersen, M; Raun, K; Hansen, B S

2001-07-23

A series of NN703 analogues with lysine mimetics combined with naphthyl- or biphenylalanine in the core has been prepared and tested in vitro in a rat pituitary cell based assay and subsequently in vivo in pigs in a single dose at 50 nmol/kg. Re-introduction of certain pharmacophores in the C-terminal of NN703, which were originally removed during optimisation for oral bioavailability, led to unexpectedly potent compounds in vitro as well as in vivo.

Physical Oceanography Report. Helicopter-Based STD Data from MIZEX 83 (Marginal Ice Zone Experiment).

DTIC Science & Technology

1984-09-01

platform /sonde-cradle support system was developed at Lamont to specifically conform to the passenger compartment ofI Unfortunately, the 202 underwater...NN o Ito~,O rd-toc,-t CD 0 000-0 N-10 6, )-0ooUo06oOo owo V 000000 WIX ; OAO. N AIN to.6uIND- 6ro"~ooo’o NK 6-ooW m 0 0 Lai 0 .NN NN I 0 Z M’%W~0~N

Detecting the Difficulty Level of Foreign Language Texts

DTIC Science & Technology

2010-02-01

continuous tenses), as well as part- of- speech labels for words. The authors used a k-Nearest Neighbor ( kNN ) classifier (Cover and Hart, 1967; Mitchell, 1997...anticipate, and influence these situations and to operate in them is found in foreign language speech and text. For this reason, military linguists are...the language model system, LGR is the prediction of one of the grammar-based classifiers, and CkNN is a confidence value of the kNN prediction for the

Second sound and the density response function in uniform superfluid atomic gases

NASA Astrophysics Data System (ADS)

Hu, H.; Taylor, E.; Liu, X.-J.; Stringari, S.; Griffin, A.

2010-04-01

Recently, there has been renewed interest in second sound in superfluid Bose and Fermi gases. By using two-fluid hydrodynamic theory, we review the density response χnn(q, ω) of these systems as a tool to identify second sound in experiments based on density probes. Our work generalizes the well-known studies of the dynamic structure factor S(q, ω) in superfluid 4He in the critical region. We show that, in the unitary limit of uniform superfluid Fermi gases, the relative weight of second versus first sound in the compressibility sum rule is given by the Landau-Placzek ratio \\epsilon_{\\mathrm{LP}}\\equiv (\\bar{c}_p-\\bar{c}_v)/\\bar{c}_v for all temperatures below Tc. In contrast to superfluid 4He, epsilonLP is much larger in strongly interacting Fermi gases, being already of order unity for T~0.8Tc, thereby providing promising opportunities to excite second sound with density probes. The relative weights of first and second sound are quite different in S(q, ω) (measured in pulse propagation studies) as compared with Imχnn(q, ω) (measured in two-photon Bragg scattering). We show that first and second sound in S(q, ω) in a strongly interacting Bose-condensed gas are similar to those in a Fermi gas at unitarity. However, in a weakly interacting Bose gas, first and second sound are mainly uncoupled oscillations of the thermal cloud and condensate, respectively, and second sound has most of the spectral weight in S(q, ω). We also discuss the behaviour of the superfluid and normal fluid velocity fields involved in first and second sound.

Partial Molar Volumes of 15-Crown-5 Ether in Mixtures of N,N-Dimethylformamide with Water.

PubMed

Tyczyńska, Magdalena; Jóźwiak, Małgorzata

2014-01-01

The density of 15-crown-5 ether (15C5) solutions in the mixtures of N,N -dimethylformamide (DMF) and water (H 2 O) was measured within the temperature range 293.15-308.15 K using an Anton Paar oscillatory U-tube densimeter. The results were used to calculate the apparent molar volumes ( V Φ ) of 15C5 in the mixtures of DMF + H 2 O over the whole concentration range. Using the apparent molar volumes and Redlich and Mayer equation, the standard partial molar volumes of 15-crown-5 were calculated at infinite dilution ([Formula: see text]). The limiting apparent molar expansibilities ( α ) were also calculated. The data are discussed from the point of view of the effect of concentration changes on interactions in solution.

A crossover in anisotropic nanomechanochemistry of van der Waals crystals

NASA Astrophysics Data System (ADS)

Shimamura, Kohei; Misawa, Masaaki; Li, Ying; Kalia, Rajiv K.; Nakano, Aiichiro; Shimojo, Fuyuki; Vashishta, Priya

2015-12-01

In nanoscale mechanochemistry, mechanical forces selectively break covalent bonds to essentially control chemical reactions. An archetype is anisotropic detonation of layered energetic molecular crystals bonded by van der Waals (vdW) interactions. Here, quantum molecular dynamics simulations reveal a crossover of anisotropic nanomechanochemistry of vdW crystal. Within 10-13 s from the passage of shock front, lateral collision produces NO2 via twisting and bending of nitro-groups and the resulting inverse Jahn-Teller effect, which is mediated by strong intra-layer hydrogen bonds. Subsequently, as we transition from heterogeneous to homogeneous mechanochemical regimes around 10-12 s, shock normal to multilayers becomes more reactive, producing H2O assisted by inter-layer N-N bond formation. These time-resolved results provide much needed atomistic understanding of nanomechanochemistry that underlies a wider range of technologies.

Immunohistochemical evidence for the involvement of protein convertases 5A and 2 in the processing of pro-neurotensin in rat brain.

PubMed

Villeneuve, P; Lafortune, L; Seidah, N G; Kitabgi, P; Beaudet, A

2000-08-28

The neuropeptides/neurotransmitters neurotensin (NT) and neuromedin (NN) are synthesized by endoproteolytic cleavage of a common inactive precursor, pro-NT/NN. In vitro studies have suggested that the prohormone convertases PC5A and PC2 might both be involved in this process. In the present study, we used dual immunohistochemical techniques to determine whether either one or both of these two convertases were co-localized with pro-NT/NN maturation products and could therefore be involved in the physiological processing of this propeptide in rat brain. PC2-immunoreactive neurons were present in all regions immunopositive for NT. All but three regions expressing NT were also immunopositive for PC5A. Dual localization of NT with either convertase revealed that NT was extensively co-localized with both PC5A and PC2, albeit with regional differences. These results strongly suggest that PC5A and PC2 may play a key role in the maturation of pro-NT/NN in mammalian brain. The regional variability in NT/PC co-localization patterns may account for the region-specific maturation profiles previously reported for pro-NT/NN. The high degree of overlap between PC5A and PC2 in most NT-rich areas further suggests that these two convertases may act jointly to process pro-NT/NN. At the subcellular level, PC5A was largely co-localized with the mid-cisternae Golgi marker MG-160. By contrast, PC2 was almost completely excluded from MG-160-immunoreactive compartments. These results suggest that PC5A, which is particularly efficient at cleaving the two C-terminal-most dibasics of pro-NT/NN, may be acting as early as in the Golgi apparatus to release NT, whereas PC2, which is considerably more active than PC5A in cleaving the third C-terminal doublet, may be predominantly involved further distally along the secretory pathway to release NN. Copyright 2000 Wiley-Liss, Inc.

Measurement of charged jet production cross sections and nuclear modification in p–Pb collisions at s NN = 5.02  TeV

DOE PAGES

Adam, J.

2015-07-26

Charged jet production cross sections in p-Pb collisions at √s(NN) = 5.02 TeV measured with the ALICE detector at the LHC are presented. Using the anti-k T algorithm, jets have been reconstructed in the central rapidity region from charged particles with resolution parameters R = 0.2 and R = 0.4. The reconstructed jets have been corrected for detector effects and the underlying event background. To calculate the nuclear modification factor, R- pPb, of charged jets in p-Pb collisions, a pp reference was constructed by scaling previously measured charged jet spectra at √s = 7 TeV. In the transverse momentum rangemore » 20 ≤ p T, ch jet ≤ 120 GeV/c, R- pPb is found to be consistent with unity, indicating the absence of strong nuclear matter effects on jet production. Major modifications to the radial jet structure are probed via the ratio of jet production cross sections reconstructed with the two different resolution parameters. In conclusion, this ratio is found to be similar to the measurement in pp collisions at √s = 7 TeV and to the expectations from PYTHIA pp simulations and NLO pQCD calculations at √s(NN) = 5.02 TeV.« less

2. Historic American Buildings Survey Frank O. Branzetti, Photographer Sept. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

2. Historic American Buildings Survey Frank O. Branzetti, Photographer Sept. 4, 1940 (nn) 18- MILE STONE ROUTE 126, WAYLAND - Milestones MM, NN & OO, Various Wayland locations, Wayland, Middlesex County, MA

Predictions for isobaric collisions at √{sN N}=200 GeV from a multiphase transport model

NASA Astrophysics Data System (ADS)

Deng, Wei-Tian; Huang, Xu-Guang; Ma, Guo-Liang; Wang, Gang

2018-04-01

The isobaric collisions of Ru9644+Ru9644 and Zr9640+Zr9640 have recently been proposed to discern the charge separation signal of the chiral magnetic effect (CME). In this article, we employ the string melting version of a multiphase transport model to predict various charged-particle observables, including d N /d η ,pT spectra, elliptic flow (v2), and particularly possible CME signals in Ru + Ru and Zr + Zr collisions at √{sNN}=200 GeV . Two sets of the nuclear structure parametrization have been explored, and the difference between the two isobaric collisions appears to be small, in terms of d N /d η ,pT spectra, and v2 for charged particles. We mimic the CME by introducing an initial charge separation that is proportional to the magnetic field produced in the collision, and study how the final-state interactions affect the CME observables. The relative difference in the CME signal between the two isobaric collisions is found to be robust, insensitive to the final-state interactions.

Protective Effect of Tetracycline against Dermal Toxicity Induced by Jellyfish Venom

PubMed Central

Kang, Changkeun; Jin, Yeung Bae; Kwak, Jeongsoo; Jung, Hongseok; Yoon, Won Duk; Yoon, Tae-Jin; Kim, Jong-Shu; Kim, Euikyung

2013-01-01

Background Previously, we have reported that most, if not all, of the Scyphozoan jellyfish venoms contain multiple components of metalloproteinases, which apparently linked to the venom toxicity. Further, it is also well known that there is a positive correlation between the inflammatory reaction of dermal tissues and their tissue metalloproteinase activity. Based on these, the use of metalloproteinase inhibitors appears to be a promising therapeutic alternative for the treatment of jellyfish envenomation. Methodology and Principal Findings Tetracycline (a metalloproteinase inhibitor) has been examined for its activity to reduce or prevent the dermal toxicity induced by Nemopilema nomurai (Scyphozoa: Rhizostomeae) jellyfish venom (NnV) using in vitro and in vivo models. HaCaT (human keratinocyte) and NIH3T3 (mouse fibroblast) incubated with NnV showed decreases in cell viability, which is associated with the inductions of metalloproteinase-2 and -9. This result suggests that the use of metalloproteinase inhibitors, such as tetracycline, may prevent the jellyfish venom-mediated local tissue damage. In vivo experiments showed that comparing with NnV-alone treatment, tetracycline pre-mixed NnV demonstrated a significantly reduced progression of dermal toxicity upon the inoculation onto rabbit skin. Conclusions/Significance It is believed that there has been no previous report on the therapeutic agent of synthetic chemical origin for the treatment of jellyfish venom-induced dermonecrosis based on understanding its mechanism of action except the use of antivenom treatment. Furthermore, the current study, for the first time, has proposed a novel mechanism-based therapeutic intervention for skin damages caused by jellyfish stings. PMID:23536767

Depression and reduced heart rate variability after cardiac surgery: the mediating role of emotion regulation.

PubMed

Patron, Elisabetta; Messerotti Benvenuti, Simone; Favretto, Giuseppe; Gasparotto, Renata; Palomba, Daniela

2014-02-01

Heart rate variability (HRV), as an index of autonomic nervous system (ANS) functioning, is reduced by depression after cardiac surgery, but the underlying mechanisms of this relationship are poorly understood. Poor emotion regulation as a core symptom of depression has also been associated with altered ANS functioning. The present study aimed to examine whether emotion dysregulation could be a mediator of the depression-reduced HRV relationship observed after cardiac surgery. Self-reported emotion regulation and four-minute HRV were measured in 25 depressed and 43 nondepressed patients after cardiac surgery. Mediation analysis was conducted to evaluate emotion regulation as a mediator of the depression-reduced HRV relationship. Compared to nondepressed patients, those with depression showed lower standard deviation of normal-to-normal (NN) intervals (p<.05), root mean square successive difference of NN intervals (p<.004), and number of interval differences of successive NN intervals greater than 50ms (NN50) (p<.05). Increased low frequency (LF) in normalized units (n.u.) and reduced high frequency (HF) n.u. were also found in depressed compared to nondepressed patients (p's<.01). Mediation analysis revealed that suppression of emotion-expressive behavior partially mediated the effect of depression on LF n.u. and HF n.u. Results confirmed previous findings showing that depression is associated with reduced HRV, especially a reduced vagal tone and a sympathovagal imbalance, after cardiac surgery. This study also provides preliminary evidence that increased trait levels of suppression of emotion-expressive behavior may mediate the depression-related sympathovagal imbalance after cardiac surgery. Copyright © 2013 Elsevier B.V. All rights reserved.

A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

NASA Astrophysics Data System (ADS)

Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.

2015-05-01

Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

PubMed

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

All Prime Contract Awards by State or Country, Place, and Contractor, FY 87. Part 21. Aberdeen, Washington-Classified Location, Domestic.

DTIC Science & Technology

1987-01-01

iNC*NNNN N I N IN (D 0~0 Nr I CJ " l l 1 1 N NN NN N * NNN NN NI NI Nl 001 I 0) 1 cl N N"N " C14 ciC4 0 1(4NIN 1 (NN NN N N IN COM 1 00 0 0 -1 000...000 I W IA I.- : COm -IN ML )0 4 n0- 1 - A10 ( N N M0 -0M n 00 44-4-4 M U) () 04 V(C)-CF JN00N)(’J4 4-4- - 4 Z M 0 0 0 0-4 0 -4 4 00 C 0 0 0-4 4 N0-4 0...0 V 0 - 004mI. I CoW00 < 0- I C O- COOOI - COLn 4 COA CO - CO C~O CO CO C)O4- COM - *I WOO< 40 -W 04 1W 4C 1W 1W -A -W 1W Z 04 MW 0 Q4 4 ., 4 0 44 1A

Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

PubMed

Adam, J; Adamová, D; Aggarwal, M M; Aglieri Rinella, G; Agnello, M; Agrawal, N; Ahammed, Z; Ahmad, S; Ahn, S U; Aiola, S; Akindinov, A; Alam, S N; Aleksandrov, D; Alessandro, B; Alexandre, D; Alfaro Molina, R; Alici, A; Alkin, A; Almaraz, J R M; Alme, J; Alt, T; Altinpinar, S; Altsybeev, I; Alves Garcia Prado, C; Andrei, C; Andronic, A; Anguelov, V; Antičić, T; Antinori, F; Antonioli, P; Aphecetche, L; Appelshäuser, H; Arcelli, S; Arnaldi, R; Arnold, O W; Arsene, I C; Arslandok, M; Audurier, B; Augustinus, A; Averbeck, R; Azmi, M D; Badalà, A; Baek, Y W; Bagnasco, S; Bailhache, R; Bala, R; Balasubramanian, S; Baldisseri, A; Baral, R C; Barbano, A M; Barbera, R; Barile, F; Barnaföldi, G G; Barnby, L S; Barret, V; Bartalini, P; Barth, K; Bartke, J; Bartsch, E; Basile, M; Bastid, N; Basu, S; Bathen, B; Batigne, G; Batista Camejo, A; Batyunya, B; Batzing, P C; Bearden, I G; Beck, H; Bedda, C; Behera, N K; Belikov, I; Bellini, F; Bello Martinez, H; Bellwied, R; Belmont, R; Belmont-Moreno, E; Belyaev, V; Benacek, P; Bencedi, G; Beole, S; Berceanu, I; Bercuci, A; Berdnikov, Y; Berenyi, D; Bertens, R A; Berzano, D; Betev, L; Bhasin, A; Bhat, I R; Bhati, A K; Bhattacharjee, B; Bhom, J; Bianchi, L; Bianchi, N; Bianchin, C; Bielčík, J; Bielčíková, J; Bilandzic, A; Biro, G; Biswas, R; Biswas, S; Bjelogrlic, S; Blair, J T; Blau, D; Blume, C; Bock, F; Bogdanov, A; Bøggild, H; Boldizsár, L; Bombara, M; Book, J; Borel, H; Borissov, A; Borri, M; Bossú, F; Botta, E; Bourjau, C; Braun-Munzinger, P; Bregant, M; Breitner, T; Broker, T A; Browning, T A; Broz, M; Brucken, E J; Bruna, E; Bruno, G E; Budnikov, D; Buesching, H; Bufalino, S; Buncic, P; Busch, O; Buthelezi, Z; Butt, J B; Buxton, J T; Caffarri, D; Cai, X; Caines, H; Calero Diaz, L; Caliva, A; Calvo Villar, E; Camerini, P; Carena, F; Carena, W; Carnesecchi, F; Castillo Castellanos, J; Castro, A J; Casula, E A R; Ceballos Sanchez, C; Cerello, P; Cerkala, J; Chang, B; Chapeland, S; Chartier, M; Charvet, J L; Chattopadhyay, S; Chattopadhyay, S; Chauvin, A; Chelnokov, V; Cherney, M; Cheshkov, C; Cheynis, B; Chibante Barroso, V; Chinellato, D D; Cho, S; Chochula, P; Choi, K; Chojnacki, M; Choudhury, S; Christakoglou, P; Christensen, C H; Christiansen, P; Chujo, T; Chung, S U; Cicalo, C; Cifarelli, L; Cindolo, F; Cleymans, J; Colamaria, F; Colella, D; Collu, A; Colocci, M; Conesa Balbastre, G; Conesa Del Valle, Z; Connors, M E; Contreras, J G; Cormier, T M; Corrales Morales, Y; Cortés Maldonado, I; Cortese, P; Cosentino, M R; Costa, F; Crochet, P; Cruz Albino, R; Cuautle, E; Cunqueiro, L; Dahms, T; Dainese, A; Danisch, M C; Danu, A; Das, D; Das, I; Das, S; Dash, A; Dash, S; De, S; De Caro, A; de Cataldo, G; de Conti, C; de Cuveland, J; De Falco, A; De Gruttola, D; De Marco, N; De Pasquale, S; Deisting, A; Deloff, A; Dénes, E; Deplano, C; Dhankher, P; Di Bari, D; Di Mauro, A; Di Nezza, P; Diaz Corchero, M A; Dietel, T; Dillenseger, P; Divià, R; Djuvsland, Ø; Dobrin, A; Domenicis Gimenez, D; Dönigus, B; Dordic, O; Drozhzhova, T; Dubey, A K; 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Görlich, L; Gotovac, S; Grabski, V; Grachov, O A; Graczykowski, L K; Graham, K L; Grelli, A; Grigoras, A; Grigoras, C; Grigoriev, V; Grigoryan, A; Grigoryan, S; Grinyov, B; Grion, N; Gronefeld, J M; Grosse-Oetringhaus, J F; Grossiord, J-Y; Grosso, R; Guber, F; Guernane, R; Guerzoni, B; Gulbrandsen, K; Gunji, T; Gupta, A; Gupta, R; Haake, R; Haaland, Ø; Hadjidakis, C; Haiduc, M; Hamagaki, H; Hamar, G; Hamon, J C; Harris, J W; Harton, A; Hatzifotiadou, D; Hayashi, S; Heckel, S T; Helstrup, H; Herghelegiu, A; Herrera Corral, G; Hess, B A; Hetland, K F; Hillemanns, H; Hippolyte, B; Horak, D; Hosokawa, R; Hristov, P; Huang, M; Humanic, T J; Hussain, N; Hussain, T; Hutter, D; Hwang, D S; Ilkaev, R; Inaba, M; Incani, E; Ippolitov, M; Irfan, M; Ivanov, M; Ivanov, V; Izucheev, V; Jacazio, N; Jacobs, P M; Jadhav, M B; Jadlovska, S; Jadlovsky, J; Jahnke, C; Jakubowska, M J; Jang, H J; Janik, M A; Jayarathna, P H S Y; Jena, C; Jena, S; Jimenez Bustamante, R T; Jones, P G; Jusko, A; Kalinak, P; Kalweit, A; Kamin, J; Kang, J H; Kaplin, V; Kar, S; Karasu Uysal, A; Karavichev, O; Karavicheva, T; Karayan, L; Karpechev, E; Kebschull, U; Keidel, R; Keijdener, D L D; Keil, M; Mohisin Khan, M; Khan, P; Khan, S A; Khanzadeev, A; Kharlov, Y; Kileng, B; Kim, D W; Kim, D J; Kim, D; Kim, H; Kim, J S; Kim, M; Kim, S; Kim, T; Kirsch, S; Kisel, I; Kiselev, S; Kisiel, A; Kiss, G; Klay, J L; Klein, C; Klein, J; Klein-Bösing, C; Klewin, S; Kluge, A; Knichel, M L; Knospe, A G; Kobdaj, C; Kofarago, M; Kollegger, T; Kolojvari, A; Kondratiev, V; Kondratyeva, N; Kondratyuk, E; Konevskikh, A; Kopcik, M; Kostarakis, P; Kour, M; Kouzinopoulos, C; Kovalenko, O; Kovalenko, V; Kowalski, M; Koyithatta Meethaleveedu, G; Králik, I; Kravčáková, A; Kretz, M; Krivda, M; Krizek, F; Kryshen, E; Krzewicki, M; Kubera, A M; Kučera, V; Kuhn, C; Kuijer, P G; Kumar, A; Kumar, J; Kumar, L; Kumar, S; Kurashvili, P; Kurepin, A; Kurepin, A B; Kuryakin, A; Kweon, M J; Kwon, Y; La Pointe, S L; La Rocca, P; Ladron de Guevara, P; Lagana Fernandes, C; Lakomov, I; Langoy, R; Lara, C; Lardeux, A; Lattuca, A; Laudi, E; Lea, R; Leardini, L; Lee, G R; Lee, S; Lehas, F; Lemmon, R C; Lenti, V; Leogrande, E; León Monzón, I; León Vargas, H; Leoncino, M; Lévai, P; Li, S; Li, X; Lien, J; Lietava, R; Lindal, S; Lindenstruth, V; Lippmann, C; Lisa, M A; Ljunggren, H M; Lodato, D F; Loenne, P I; Loginov, V; Loizides, C; Lopez, X; López Torres, E; Lowe, A; Luettig, P; Lunardon, M; Luparello, G; Lutz, T H; Maevskaya, A; Mager, M; Mahajan, S; Mahmood, S M; Maire, A; Majka, R D; Malaev, M; Maldonado Cervantes, I; Malinina, L; Mal'Kevich, D; Malzacher, P; Mamonov, A; Manko, V; Manso, F; Manzari, V; Marchisone, M; Mareš, J; Margagliotti, G V; Margotti, A; Margutti, J; Marín, A; Markert, C; Marquard, M; Martin, N A; Martin Blanco, J; Martinengo, P; Martínez, M I; Martínez García, G; Martinez Pedreira, M; Mas, A; Masciocchi, S; Masera, M; Masoni, A; Massacrier, L; Mastroserio, A; Matyja, A; Mayer, C; Mazer, J; Mazzoni, M A; Mcdonald, D; Meddi, F; Melikyan, Y; Menchaca-Rocha, A; Meninno, E; Mercado Pérez, J; Meres, M; Miake, Y; Mieskolainen, M M; Mikhaylov, K; Milano, L; Milosevic, J; Minervini, L M; Mischke, A; Mishra, A N; Miśkowiec, D; Mitra, J; Mitu, C M; Mohammadi, N; Mohanty, B; Molnar, L; Montaño Zetina, L; Montes, E; Moreira De Godoy, D A; Moreno, L A P; Moretto, S; Morreale, A; Morsch, A; Muccifora, V; Mudnic, E; Mühlheim, D; Muhuri, S; Mukherjee, M; Mulligan, J D; Munhoz, M G; Munzer, R H; Murakami, H; Murray, S; Musa, L; Musinsky, J; Naik, B; Nair, R; Nandi, B K; Nania, R; Nappi, E; Naru, M U; Natal da Luz, H; Nattrass, C; Navarro, S R; Nayak, K; Nayak, R; Nayak, T K; Nazarenko, S; Nedosekin, A; Nellen, L; Ng, F; Nicassio, M; Niculescu, M; Niedziela, J; Nielsen, B S; Nikolaev, S; Nikulin, S; Nikulin, V; Noferini, F; Nomokonov, P; Nooren, G; Noris, J C C; Norman, J; Nyanin, A; Nystrand, J; Oeschler, H; Oh, S; Oh, S K; Ohlson, A; Okatan, A; Okubo, T; Olah, L; Oleniacz, J; Oliveira Da Silva, A C; Oliver, M H; Onderwaater, J; Oppedisano, C; Orava, R; Ortiz Velasquez, A; Oskarsson, A; Otwinowski, J; Oyama, K; Ozdemir, M; Pachmayer, Y; Pagano, P; Paić, G; Pal, S K; Pan, J; Pandey, A K; Papikyan, V; Pappalardo, G S; Pareek, P; Park, W J; Parmar, S; Passfeld, A; Paticchio, V; Patra, R N; Paul, B; Pei, H; Peitzmann, T; Pereira Da Costa, H; Peresunko, D; Pérez Lara, C E; Perez Lezama, E; Peskov, V; Pestov, Y; Petráček, V; Petrov, V; Petrovici, M; Petta, C; Piano, S; Pikna, M; Pillot, P; Pimentel, L O D L; Pinazza, O; Pinsky, L; Piyarathna, D B; Płoskoń, M; Planinic, M; Pluta, J; Pochybova, S; Podesta-Lerma, P L M; Poghosyan, M G; Polichtchouk, B; Poljak, N; Poonsawat, W; Pop, A; Porteboeuf-Houssais, S; Porter, J; Pospisil, J; Prasad, S K; Preghenella, R; Prino, F; Pruneau, C A; Pshenichnov, I; Puccio, M; Puddu, G; Pujahari, P; Punin, V; Putschke, J; Qvigstad, H; Rachevski, A; Raha, S; Rajput, S; Rak, J; Rakotozafindrabe, A; Ramello, L; Rami, F; Raniwala, R; Raniwala, S; Räsänen, S S; Rascanu, B T; Rathee, D; Read, K F; Redlich, K; 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Wiechula, J; Wikne, J; Wilk, G; Wilkinson, J; Williams, M C S; Windelband, B; Winn, M; Yang, H; Yang, P; Yano, S; Yasar, C; Yin, Z; Yokoyama, H; Yoo, I-K; Yoon, J H; Yurchenko, V; Yushmanov, I; Zaborowska, A; Zaccolo, V; Zaman, A; Zampolli, C; Zanoli, H J C; Zaporozhets, S; Zardoshti, N; Zarochentsev, A; Závada, P; Zaviyalov, N; Zbroszczyk, H; Zgura, I S; Zhalov, M; Zhang, H; Zhang, X; Zhang, Y; Zhang, C; Zhang, Z; Zhao, C; Zhigareva, N; Zhou, D; Zhou, Y; Zhou, Z; Zhu, H; Zhu, J; Zichichi, A; Zimmermann, A; Zimmermann, M B; Zinovjev, G; Zyzak, M

2016-06-03

The pseudorapidity density of charged particles, dN_{ch}/dη, at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. For the 5% most central collisions, we measure a value of 1943±54. The rise in dN_{ch}/dη as a function of sqrt[s_{NN}] is steeper than that observed in proton-proton collisions and follows the trend established by measurements at lower energy. The increase of dN_{ch}/dη as a function of the average number of participant nucleons, ⟨N_{part}⟩, calculated in a Glauber model, is compared with the previous measurement at sqrt[s_{NN}]=2.76  TeV. A constant factor of about 1.2 describes the increase in dN_{ch}/dη from sqrt[s_{NN}]=2.76 to 5.02 TeV for all centrality classes, within the measured range of 0%-80% centrality. The results are also compared to models based on different mechanisms for particle production in nuclear collisions.

Excitation functions of parameters extracted from three-source (net-)proton rapidity distributions in Au-Au and Pb-Pb collisions over an energy range from AGS to RHIC

NASA Astrophysics Data System (ADS)

Gao, Li-Na; Liu, Fu-Hu; Sun, Yan; Sun, Zhu; Lacey, Roy A.

2017-03-01

Experimental results of the rapidity spectra of protons and net-protons (protons minus antiprotons) emitted in gold-gold (Au-Au) and lead-lead (Pb-Pb) collisions, measured by a few collaborations at the alternating gradient synchrotron (AGS), super proton synchrotron (SPS), and relativistic heavy ion collider (RHIC), are described by a three-source distribution. The values of the distribution width σC and fraction kC of the central rapidity region, and the distribution width σF and rapidity shift Δ y of the forward/backward rapidity regions, are then obtained. The excitation function of σC increases generally with increase of the center-of-mass energy per nucleon pair √{s_{NN}}. The excitation function of σF shows a saturation at √{s_{NN}}=8.8 GeV. The excitation function of kC shows a minimum at √{s_{NN}}=8.8 GeV and a saturation at √{s_{NN}} ≈ 17 GeV. The excitation function of Δ y increases linearly with ln(√{s_{NN}}) in the considered energy range.

Old Torreon Navajo Day School, Cuba, NM: NN0030341

EPA Pesticide Factsheets

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030341 to Bureau of Indian Affairs Old Torreon Navajo Day School Wastewater Treatment Lagoon.

An array of correlated atom interferometers to study to study the local fluctuations of the gravitational field and its impact on low fequency gravitational waves observations

NASA Astrophysics Data System (ADS)

Bouyer, P.; Canuel, B.; Pelisson, S.; Harms, J.; Bertoldi, A.; Gaffet, S.; Landragin, A.; Lefevre, G.; Riou, I.; Geiger, R.

2016-12-01

We will present here the Matter-Wave laser Interferometer Gravitation Antenna, MIGA, a hybrid instrument composed of a network of atom interferometers horizontally aligned and interrogated by the resonant field of an optical cavity. This detector will provide measurements of sub Hertz variations of the gravitational strain tensor. MIGA will bring new methods for geophysics for the characterization of spatial and temporal variations of the local gravity field and will also be a demonstrator for future low frequency Gravitational Wave (GW) detections. The recent first direct observation of gravitational radiation opens the way towards a novel astronomy requires a new class of low frequency Gravitational Wave detectors such as MIGA. Nevertheless, the fluctuations of the Earth gravitational field over different baselines are of high relevance for the functioning of such experiments. Indeed, a fluctuating gravity gradient causes a tidal effect that cannot, in principle, be distinguished from Gravitational Waves.This so-called « Newtonian Noise » is therefore considered up to now as a fundamental limit for any ground based detector and the main reason for restricting future low frequency GW detectors to space. Nevertheless, these two contributions may become discernible by the use of a network of test masses. Indeed, both GW and NN effects will have different spatial signatures over the test mass network. While GW has extremely long characteristic length, NN has shorter characteristic lengths going from the meter to a few kilometers.The array of distant Atom Interferometers in MIGA can be used as network of test masses, which can be correlated using a common laser link. Differential measurements between the atom interferometers of the Network enables for a large reduction of the effect of NN and opens the way towards the realization of low frequency GW detectors.In this paper, we will detail the projection of background NN in the underground environment of the LSBB and present GW interferometer geometries enabling its reduction.

Resonance charge transfer, transport cross sections, and collision integrals for N(+)(3P)-N(4S0) and O(+)(4S0)-O(3P) interactions

NASA Technical Reports Server (NTRS)

Stallcop, James R.; Partridge, Harry; Levin, Eugene

1991-01-01

N2(+) and O2(+) potential energy curves have been constructed by combining measured data with the results from electronic structure calculations. These potential curves have been employed to determine accurate charge exchange cross sections, transport cross sections, and collision integrals for ground state N(+)-N and O(+)-O interactions. The cross sections have been calculated from a semiclassical approximation to the scattering using a computer code that fits a spline curve through the discrete potential data and incorporates the proper long-range behavior of the interactions forces. The collision integrals are tabulated for a broad range of temperatures 250-100,000 K and are intended to reduce the uncertainty in the values of the transport properties of nonequilibrium air, particularly at high temperatures.

A Nonlinear Theory for Asymmetric Sandwich Plates with a First-Order Compressible Core Impacted by a Friedlander-Type Shock Loading (PREPRINT)

DTIC Science & Technology

2011-07-25

the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the...ntN (27b) 0dnu or 0 d nnN (27c) 0 dtu or 0 d ntN (27d) 03  au or  a tnt a nnn d nt d t d nn d n a nt a t a nn a n MMNuNuNuNu

Numerical Studies of the Radiation Condition at the Inlet of a Transonic Compressor Blade Row.

DTIC Science & Technology

1980-04-01

284-296 (1977). 2 aL.) ITH~rOU7 REFLECTOAJ &RWD b)WITH PJEFL.ECr-IOA, FIGLJ/AE I NV9 PATTERNhS EXPEC TED located a chord length or so upstream of the...C) W In -0N -W e --N 0 -00 e -’N -" M-M M V -NM ""NM N NM 0 - - - - - - - - - - - - --4 WUN NWCON NMWN NN VN N-CYN N N NOJcyN N-N N-N N O~j N 000 00

Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Zhang, Li-jie

2017-10-01

Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

Measurements of J/ψ Production and Polarization in p+p and p+Au Collisions at s NN = 200 GeV with the STAR Experiment

NASA Astrophysics Data System (ADS)

Liu, Zhen

We present the measurements of J/ψ production at mid-rapidity via the di-muon decay channel in p+p and p+Au collisions at s NN = 200 GeV by the STAR experiment at RHIC. In p+p collisions, the measured inclusive J/ψ cross section can be qualitatively described by model calculations. The J/ψ polarization parameters, λ𝜃, λϕ as well as the frame-invariant quantity λinv, are presented as a function of transverse momentum in both the helicity and Collins-Soper frames. No significant polarization is observed. In addition, the nuclear modification factor for inclusive J/ψ in p+Au collisions is similar to that measured in d+Au collisions and favors an additional nuclear absorption effect on top of the nuclear PDF effect.

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Neural network based online simultaneous policy update algorithm for solving the HJI equation in nonlinear H∞ control.

PubMed

Wu, Huai-Ning; Luo, Biao

2012-12-01

It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm.

Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Luy, N. T.

2018-04-01

The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Improved Monte Carlo Glauber predictions at present and future nuclear colliders

NASA Astrophysics Data System (ADS)

Loizides, Constantin; Kamin, Jason; d'Enterria, David

2018-05-01

We present the results of an improved Monte Carlo Glauber (MCG) model of relevance for collisions involving nuclei at center-of-mass energies of the BNL Relativistic Heavy Ion Collider (√{sNN}=0.2 TeV), CERN Large Hadron Collider (LHC) (√{sNN}=2.76 -8.8 TeV ), and proposed future hadron colliders (√{sNN}≈10 -63 TeV). The inelastic p p cross sections as a function of √{sNN} are obtained from a precise data-driven parametrization that exploits the many available measurements at LHC collision energies. We describe the nuclear density of a lead nucleus with two separated two-parameter Fermi distributions for protons and neutrons to account for their different densities close to the nuclear periphery. Furthermore, we model the nucleon degrees of freedom inside the nucleus through a lattice with a minimum nodal separation, combined with a "recentering and reweighting" procedure, that overcomes some limitations of previous MCG approaches. The nuclear overlap function, number of participant nucleons and binary nucleon-nucleon collisions, participant eccentricity and triangularity, overlap area, and average path length are presented in intervals of percentile centrality for lead-lead (PbPb) and proton-lead (p Pb ) collisions at all collision energies. We demonstrate for collisions at √{sNN}=5.02 TeV that the central values of the Glauber quantities change by up to 7% in a few bins of reaction centrality, due to the improvements implemented, though typically they remain within the previously assigned systematic uncertainties, while their new associated uncertainties are generally smaller (mostly below 5%) at all centralities than for earlier calculations. Tables for all quantities versus centrality at present and foreseen collision energies involving Pb nuclei, as well as for collisions of XeXe at √{sNN}=5.44 TeV , and AuAu and CuCu at √{sNN}=0.2 TeV , are provided. The source code for the improved Monte Carlo Glauber model is made publicly available.

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

NASA Astrophysics Data System (ADS)

Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.

BIA Wingate High School WWTF, Fort Wingate, NM: NN0020958

EPA Pesticide Factsheets

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0020958 to Bureau of Indian Affairs (BIA) Wingate High School Wastewater Treatment Lagoon, Fort Wingate, NM.

e+e‑ Pair Production at Very Low Transverse Mometum in Au+Au Collisions at s NN = 200 GeV and U+U Collisions at sNN = 193 GeV at STAR

NASA Astrophysics Data System (ADS)

Yang, Shuai

We present the first measurements of e+e‑ pair production at very low transverse momentum (pT < 0.15 GeV/c) in Au + Au collisions at sNN = 200 GeV and U + U collisions at sNN = 193 GeV using the STAR detector at the Relativistic Heavy Ion Collider. A significant excess, with respect to known hadronic contributions, is observed in 60-80% central heavy-ion collisions over the whole Mee range. Remarkably, the excess almost entirely happens below pT ≈ 0.15 GeV/c, and can not be explained by a theoretical model calculation incorporating in-medium broadened ρ spectral function. Moreover, the observed excess yield has no significant centrality dependence. In addition, the steepness of pT2 distribution exhibits mild invariant mass and collision species dependence.

Suppression of hadrons with large transverse momentum in central Au+Au collisions at root square[s(NN)] = 130 GeV.

PubMed

Adcox, K; Adler, S S; Ajitanand, N N; Akiba, Y; Alexander, J; Aphecetche, L; Arai, Y; Aronson, S H; Averbeck, R; Awes, T C; Barish, K N; Barnes, P D; Barrette, J; Bassalleck, B; Bathe, S; Baublis, V; Bazilevsky, A; Belikov, S; Bellaiche, F G; Belyaev, S T; Bennett, M J; Berdnikov, Y; Botelho, S; Brooks, M L; Brown, D S; Bruner, N; Bucher, D; Buesching, H; Bumazhnov, V; Bunce, G; Burward-Hoy, J; Butsyk, S; Carey, T A; Chand, P; Chang, J; Chang, W C; Chavez, L L; Chernichenko, S; Chi, C Y; Chiba, J; Chiu, M; Choudhury, R K; Christ, T; Chujo, T; Chung, M S; Chung, P; Cianciolo, V; Cole, B A; D'Enterria, D G; David, G; Delagrange, H; Denisov, A; Deshpande, A; Desmond, E J; Dietzsch, O; Dinesh, B V; Drees, A; Durum, A; Dutta, D; Ebisu, K; Efremenko, Y V; El Chenawi, K; En'yo, H; Esumi, S; Ewell, L; Ferdousi, T; Fields, D E; Fokin, S L; Fraenkel, Z; Franz, A; Frawley, A D; Fung, S-Y; Garpman, S; Ghosh, T K; Glenn, A; Godoi, A L; Goto, Y; Greene, S V; Grosse Perdekamp, M; Gupta, S K; Guryn, W; Gustafsson, H-A; Haggerty, J S; Hamagaki, H; Hansen, A G; Hara, H; Hartouni, E P; Hayano, R; Hayashi, N; He, X; Hemmick, T K; Heuser, J M; Hibino, M; Hill, J C; Ho, D S; Homma, K; Hong, B; Hoover, A; Ichihara, T; Imai, K; Ippolitov, M S; Ishihara, M; Jacak, B V; Jang, W Y; Jia, J; Johnson, B M; Johnson, S C; Joo, K S; Kametani, S; Kang, J H; Kann, M; Kapoor, S S; Kelly, S; Khachaturov, B; Khanzadeev, A; Kikuchi, J; Kim, D J; Kim, H J; Kim, S Y; Kim, Y G; Kinnison, W W; Kistenev, E; Kiyomichi, A; Klein-Boesing, C; Klinksiek, S; Kochenda, L; Kochetkov, V; Koehler, D; Kohama, T; Kotchetkov, D; Kozlov, A; Kroon, P J; Kurita, K; Kweon, M J; Kwon, Y; Kyle, G S; Lacey, R; Lajoie, J G; Lauret, J; Lebedev, A; Lee, D M; Leitch, M J; Li, X H; Li, Z; Lim, D J; Liu, M X; Liu, X; Liu, Z; Maguire, C F; Mahon, J; Makdisi, Y I; Manko, V I; Mao, Y; Mark, S K; Markacs, S; Martinez, G; Marx, M D; Masaike, A; Matathias, F; Matsumoto, T; McGaughey, P L; Melnikov, E; Merschmeyer, M; Messer, F; Messer, M; Miake, Y; Miller, T E; Milov, A; Mioduszewski, S; Mischke, R E; Mishra, G C; Mitchell, J T; Mohanty, A K; Morrison, D P; Moss, J M; Mühlbacher, F; Muniruzzaman, M; Murata, J; Nagamiya, S; Nagasaka, Y; Nagle, J L; Nakada, Y; Nandi, B K; Newby, J; Nikkinen, L; Nilsson, P; Nishimura, S; Nyanin, A S; Nystrand, J; O'Brien, E; Ogilvie, C A; Ohnishi, H; Ojha, I D; Ono, M; Onuchin, V; Oskarsson, A; Osterman, L; Otterlund, I; Oyama, K; Paffrath, L; Palounek, A P T; Pantuev, V S; Papavassiliou, V; Pate, S F; Peitzmann, T; Petridis, A N; Pinkenburg, C; Pisani, R P; Pitukhin, P; Plasil, F; Pollack, M; Pope, K; Purschke, M L; Ravinovich, I; Read, K F; Reygers, K; Riabov, V; Riabov, Y; Rosati, M; Rose, A A; Ryu, S S; Saito, N; Sakaguchi, A; Sakaguchi, T; Sako, H; Sakuma, T; Samsonov, V; Sangster, T C; Santo, R; Sato, H D; Sato, S; Sawada, S; Schlei, B R; Schutz, Y; Semenov, V; Seto, R; Shea, T K; Shein, I; Shibata, T-A; Shigaki, K; Shiina, T; Shin, Y H; Sibiriak, I G; Silvermyr, D; Sim, K S; Simon-Gillo, J; Singh, C P; Singh, V; Sivertz, M; Soldatov, A; Soltz, R A; Sorensen, S; Stankus, P W; Starinsky, N; Steinberg, P; Stenlund, E; Ster, A; Stoll, S P; Sugioka, M; Sugitate, T; Sullivan, J P; Sumi, Y; Sun, Z; Suzuki, M; Takagui, E M; Taketani, A; Tamai, M; Tanaka, K H; Tanaka, Y; Taniguchi, E; Tannenbaum, M J; Thomas, J; Thomas, J H; Thomas, T L; Tian, W; Tojo, J; Torii, H; Towell, R S; Tserruya, I; Tsuruoka, H; Tsvetkov, A A; Tuli, S K; Tydesjö, H; Tyurin, N; Ushiroda, T; van Hecke, H W; Velissaris, C; Velkovska, J; Velkovsky, M; Vinogradov, A A; Volkov, M A; Vorobyov, A; Vznuzdaev, E; Wang, H; Watanabe, Y; White, S N; Witzig, C; Wohn, F K; Woody, C L; Xie, W; Yagi, K; Yokkaichi, S; Young, G R; Yushmanov, I E; Zajc, W A; Zhang, Z; Zhou, S

2002-01-14

Transverse momentum spectra for charged hadrons and for neutral pions in the range 1 GeV/c

Adaptive critic neural network-based object grasping control using a three-finger gripper.

PubMed

Jagannathan, S; Galan, Gustavo

2004-03-01

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.

Differential catabolic fate of neuromedin N and neurotensin in the canine intestinal mucosa.

PubMed

Barelli, H; Mao, Y K; Vincent, B; Daniel, E E; Vincent, J P; Checler, F

1993-01-01

We have established the peptidase content of a P2 fraction (enriched in synaptosomes) and plasma membranes prepared from canine intestinal mucosa. Fourteen exo- and endopeptidases were assayed with fluorimetric or chromogenic substrates and identified by means of specific peptidase inhibitors. Post-proline dipeptidyl aminopeptidase IV, aminopeptidase M, and carboxypeptidase A were the most abundant exopeptidases, while aminopeptidases A and B, dipeptidyl aminopeptidase, pyroglutamyl peptide hydrolase I, and carboxypeptidase B displayed little, if any, activity. Endopeptidase 24.11 was the only endopeptidase that was detected in high amount. By contrast, proline endopeptidase exhibited a low activity, while angiotensin-converting enzyme, endopeptidase 24.15, endopeptidase 24.16, and cathepsin B and D-like activities were not detected. The catabolic rates of the two related neuropeptides, neurotensin (NT) and neuromedin N (NN), established that NN was inactivated 16 to 24 times faster than NT by plasma membrane and P2 fractions, respectively. Furthermore, the two peptides underwent qualitatively distinct mechanisms of degradation. A phosphoramidon-sensitive formation of NT(1-10) was detected as the major NT catabolite, indicating that NT was susceptible to an endoproteolytic cleavage elicited by endopeptidase 24.11. By contrast, NN was inactivated by the action of an exopeptidase at its N-terminus, leading to the formation of [des-Lys1]NN. The occurrence of this NN metabolite was prevented by bestatin and actinonin, but not by the aminopeptidase B inhibitor, arphamenine B, indicating that the release of the N-terminal residue of NN was likely due to aminopeptidase M.(ABSTRACT TRUNCATED AT 250 WORDS)

DFMSPH14: A C-code for the double folding interaction potential of two spherical nuclei

NASA Astrophysics Data System (ADS)

Gontchar, I. I.; Chushnyakova, M. V.

2016-09-01

This is a new version of the DFMSPH code designed to obtain the nucleus-nucleus potential by using the double folding model (DFM) and in particular to find the Coulomb barrier. The new version uses the charge, proton, and neutron density distributions provided by the user. Also we added an option for fitting the DFM potential by the Gross-Kalinowski profile. The main functionalities of the original code (e.g. the nucleus-nucleus potential as a function of the distance between the centers of mass of colliding nuclei, the Coulomb barrier characteristics, etc.) have not been modified. Catalog identifier: AEFH_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFH_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland. Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 7211 No. of bytes in distributed program, including test data, etc.: 114404 Distribution format: tar.gz Programming language: C Computer: PC and Mac Operation system: Windows XP and higher, MacOS, Unix/Linux Memory required to execute with typical data: below 10 Mbyte Classification: 17.9 Catalog identifier of previous version: AEFH_v1_0 Journal reference of previous version: Comp. Phys. Comm. 181 (2010) 168 Does the new version supersede the previous version?: Yes Nature of physical problem: The code calculates in a semimicroscopic way the bare interaction potential between two colliding spherical nuclei as a function of the center of mass distance. The height and the position of the Coulomb barrier are found. The calculated potential is approximated by an analytical profile (Woods-Saxon or Gross-Kalinowski) near the barrier. Dependence of the barrier parameters upon the characteristics of the effective NN forces (like, e.g. the range of the exchange part of the nuclear term) can be investigated. Method of solution: The nucleus-nucleus potential is calculated using the double folding model with the Coulomb and the effective M3Y NN interactions. For the direct parts of the Coulomb and the nuclear terms, the Fourier transform method is used. In order to calculate the exchange parts, the density matrix expansion method is applied. Typical running time: less than 1 minute. Reason for new version: Many users asked us how to implement their own density distributions in the DFMSPH. Now this option has been added. Also we found that the calculated Double-Folding Potential (DFP) is approximated more accurately by the Gross-Kalinowski (GK) profile. This option has been also added.

Theoretical studies of urea adsorption on single wall boron-nitride nanotubes

NASA Astrophysics Data System (ADS)

Chermahini, Alireza Najafi; Teimouri, Abbas; Farrokhpour, Hossein

2014-11-01

Surface modification of a boron nitride nanotube (BNNT) with urea molecule was investigated in terms of its energetic, geometric, and electronic properties using B3LYP and PW91 density functionals. In this investigation, various armchair (n,n) nanotubes, where n = 5, 6, 7 have been used. Two different interaction modes, including interaction with outer layer and inner layer of tube were studied. The results indicated that the adsorption of single urea molecule in all of its configurations is observed to be exothermic and physical in nature. Interestingly, the adsorption energy for the most stable configuration of urea was observed when the molecule located inside of the nanotube. Besides, the adsorption of urea on BNNTs changes the conductivity of nanotube.

Chevron Mining, Inc. McKinley Mine, Gallup, NM: NN0029386

EPA Pesticide Factsheets

NPDES Permit #NN0029386 for the Chevron Mining, Inc. McKinley Mine to tributaries to the Puerco River located on Tribal, private, and public lands within the Navajo Nation and near the Arizona border in New Mexico.

Use of carbosilane dendrimer to switch macrophage polarization for the acquisition of antitumor functions

NASA Astrophysics Data System (ADS)

Perisé-Barrios, Ana J.; Gómez, Rafael; Corbí, Angel L.; de La Mata, Javier; Domínguez-Soto, Angeles; Muñoz-Fernandez, María A.

2015-02-01

Tumor microenvironment favors the escape from immunosurveillance by promoting immunosuppression and blunting pro-inflammatory responses. Since most tumor-associated macrophages (TAM) exhibit an M2-like tumor cell growth promoting polarization, we have studied the role of 2G-03NN24 carbosilane dendrimer in M2 macrophage polarization to evaluate the potential application of dendrimers in tumor immunotherapy. We found that the 2G-03NN24 dendrimer decreases LPS-induced IL-10 production from in vitro generated monocyte-derived M2 macrophages, and also switches their gene expression profile towards the acquisition of M1 polarization markers (INHBA, SERPINE1, FLT1, EGLN3 and ALDH1A2) and the loss of M2 polarization-associated markers (EMR1, IGF1, FOLR2 and SLC40A1). Furthermore, 2G-03NN24 dendrimer decreases STAT3 activation. Our results indicate that the 2G-03NN24 dendrimer can be a useful tool for antitumor therapy by virtue of its potential ability to limit the M2-like polarization of TAM.Tumor microenvironment favors the escape from immunosurveillance by promoting immunosuppression and blunting pro-inflammatory responses. Since most tumor-associated macrophages (TAM) exhibit an M2-like tumor cell growth promoting polarization, we have studied the role of 2G-03NN24 carbosilane dendrimer in M2 macrophage polarization to evaluate the potential application of dendrimers in tumor immunotherapy. We found that the 2G-03NN24 dendrimer decreases LPS-induced IL-10 production from in vitro generated monocyte-derived M2 macrophages, and also switches their gene expression profile towards the acquisition of M1 polarization markers (INHBA, SERPINE1, FLT1, EGLN3 and ALDH1A2) and the loss of M2 polarization-associated markers (EMR1, IGF1, FOLR2 and SLC40A1). Furthermore, 2G-03NN24 dendrimer decreases STAT3 activation. Our results indicate that the 2G-03NN24 dendrimer can be a useful tool for antitumor therapy by virtue of its potential ability to limit the M2-like polarization of TAM. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr04038d

The CST: Its Achievements and Its Connection to the Light Cone

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

Gross, Franz

since its inception in 1969, I have reviewed applications of the Covariant Spectator Theory (CST). The applications I discuss here include calculations of NN scattering, 3N bound states, electro- magnetic form factors of few-nucleon systems, and the recent successes in describing the dynamical generation of quark mass and the meson spectrum using a chirially invariant quark-antiquark interaction that includes confinement. Furthermore I will discuss the common origin of the Light Cone technique and the CST, which dates back to the 1970's.

The CST: Its Achievements and Its Connection to the Light Cone

DOE PAGES

Gross, Franz

2017-01-19

since its inception in 1969, I have reviewed applications of the Covariant Spectator Theory (CST). The applications I discuss here include calculations of NN scattering, 3N bound states, electro- magnetic form factors of few-nucleon systems, and the recent successes in describing the dynamical generation of quark mass and the meson spectrum using a chirially invariant quark-antiquark interaction that includes confinement. Furthermore I will discuss the common origin of the Light Cone technique and the CST, which dates back to the 1970's.

Development of high efficiency (14 percent) solar cell array module

NASA Technical Reports Server (NTRS)

Iles, P. A.; Khemthong, S.; Olah, S.; Sampson, W. J.; Ling, K. S.

1980-01-01

Most effort was concentrated on development of procedures to provide large area (3 in. diameter) high efficiency (16.5 percent AM1, 28 C) P+NN+ solar cells. Intensive tests with 3 in. slices gave consistently lower efficiency (13.5 percent). The problems were identified as incomplete formation of and optimum back surface field (BSF), and interaction of the BSF process and the shallow P+ junction. The problem was shown not to be caused by reduced quality of silicon near the edges of the larger slices.

Out-of-plane quasielastic scattering from deuterium using polarized electrons

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

Dolfini, S.; Alarcon, R.; Arenhoevel, H.

1995-06-01

We have measured the coincidence {ital d}({ital {rvec e}},{ital e}{prime}{ital p}) reaction in quasielastic scattering, detecting the proton in a noncoplanar geometry. The electron helicity asymmetry {ital A}{sub {ital e}} and the imaginary part of the longitudinal-transverse interference structure function {ital f}{sub {ital L}{ital T}}{sup {prime}} have been determined at a four-momentum transfer {ital Q}{sup 2}=3.3 fm{sup {minus}2}. The results are compared with theoretical calculations which use realistic potentials for the {ital NN} interaction.

Charge independence, charge symmetry breaking in the S-wave nucleon-nucleon interaction, and renormalization

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

Alvaro Calle Cordon,Manuel Pavon Valderrama,Enrique Ruiz Arriola

2012-02-01

We study the interplay between charge symmetry breaking and renormalization in the NN system for S-waves. We find a set of universality relations which disentangle explicitly the known long distance dynamics from low energy parameters and extend them to the Coulomb case. We analyze within such an approach the One-Boson-Exchange potential and the theoretical conditions which allow to relate the proton-neutron, proton-proton and neutron-neutron scattering observables without the introduction of extra new parameters and providing good phenomenological success.

A phenomenological π-p scattering length from pionic hydrogen

NASA Astrophysics Data System (ADS)

Ericson, T. E. O.; Loiseau, B.; Wycech, S.

2004-07-01

We derive a closed, model independent, expression for the electromagnetic correction factor to a phenomenological hadronic scattering length ah extracted from a hydrogenic atom. It is obtained in a non-relativistic approach and in the limit of a short ranged hadronic interaction to terms of order α2logα using an extended charge distribution. A hadronic πN scattering length ahπ-p=0.0870(5)mπ-1 is deduced leading to a πNN coupling constant from the GMO relation gc2/(4π)=14.04(17).

a Phenomenological Determination of the Pion-Nucleon Scattering Lengths from Pionic Hydrogen

NASA Astrophysics Data System (ADS)

Ericson, T. E. O.; Loiseau, B.; Wycech, S.

A model independent expression for the electromagnetic corrections to a phenomenological hadronic pion-nucleon (πN) scattering length ah, extracted from pionic hydrogen, is obtained. In a non-relativistic approach and using an extended charge distribution, these corrections are derived up to terms of order α2 log α in the limit of a short-range hadronic interaction. We infer ahπ ^-p=0.0870(5)m-1π which gives for the πNN coupling through the GMO relation g2π ^± pn/(4π )=14.04(17).

Theoretical/experimental comparison of deep tunneling decay of quasi-bound H(D)OCO to H(D) + CO₂.

PubMed

Wagner, Albert F; Dawes, Richard; Continetti, Robert E; Guo, Hua

2014-08-07

The measured H(D)OCO survival fractions of the photoelectron-photofragment coincidence experiments by the Continetti group are qualitatively reproduced by tunneling calculations to H(D) + CO2 on several recent ab initio potential energy surfaces for the HOCO system. The tunneling calculations involve effective one-dimensional barriers based on steepest descent paths computed on each potential energy surface. The resulting tunneling probabilities are converted into H(D)OCO survival fractions using a model developed by the Continetti group in which every oscillation of the H(D)-OCO stretch provides an opportunity to tunnel. Four different potential energy surfaces are examined with the best qualitative agreement with experiment occurring for the PIP-NN surface based on UCCSD(T)-F12a/AVTZ electronic structure calculations and also a partial surface constructed for this study based on CASPT2/AVDZ electronic structure calculations. These two surfaces differ in barrier height by 1.6 kcal/mol but when matched at the saddle point have an almost identical shape along their reaction paths. The PIP surface is a less accurate fit to a smaller ab initio data set than that used for PIP-NN and its computed survival fractions are somewhat inferior to PIP-NN. The LTSH potential energy surface is the oldest surface examined and is qualitatively incompatible with experiment. This surface also has a small discontinuity that is easily repaired. On each surface, four different approximate tunneling methods are compared but only the small curvature tunneling method and the improved semiclassical transition state method produce useful results on all four surfaces. The results of these two methods are generally comparable and in qualitative agreement with experiment on the PIP-NN and CASPT2 surfaces. The original semiclassical transition state theory method produces qualitatively incorrect tunneling probabilities on all surfaces except the PIP. The Eckart tunneling method uses the least amount of information about the reaction path and produces too high a tunneling probability on PIP-NN surface, leading to survival fractions that peak at half their measured values.

The Accuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Kong, Jeffrey

1994-01-01

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

Northern Edge Navajo Casino, Fruitland, NM: NN0030343

EPA Pesticide Factsheets

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030343) to the Navajo Tribal Utility Authority Northern Edge Navajo Casino Wastewater Treatment Facility, 2752 Indian Service Road 36, Fruitland, NM.

Elliptic flow of electrons from beauty-hadron decays extracted from Pb-Pb collision data at √{s_NN} = 2.76 TeV

NASA Astrophysics Data System (ADS)

Moreira de Godoy, D.; Herrmann, F.; Klasen, M.; Klein-Bösing, C.; Suaide, A. A. P.

2018-05-01

We present a calculation of the elliptic flow of electrons from beauty-hadron decays in semi-central Pb-Pb collisions at centre-of-mass energy per colliding nucleon pair, represented as √{s_NN}, of 2.76 TeV. The result is obtained by the subtraction of the charm-quark contribution in the elliptic flow of electrons from heavy-flavour hadron decays in semi-central Pb-Pb collisions at √{s_NN} = 2.76 TeV recently made publicly available by the ALICE collaboration.

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

DTIC Science & Technology

1999-05-17

Experimental Results In this section, we compare kNN -mut which uses the weight vector obtained using mutual information as the fi- nal weight vector and...WAKNN against kNN , C4.5 [Qui93], RIPPER [Coh95], PEBLS [CS93], Rainbow [McC96], VSM [Low95] on several synthetic and real data sets. VSM is another k...obtained without this option. 3 C4.5 RIPPER PEBLS Rainbow kNN WAKNN Syn-1 100.0 100.0 100.0 100.0 77.3 100.0 Syn-2 67.5 69.5 62.0 50.0 66.0 68.8 Syn

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

DOE PAGES

Strunk, Jacob L.; Gould, Peter J.; Packalen, Petteri; ...

2017-11-16

While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination ( R 2),more » and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km 2 Savannah River Site in South Carolina, USA. In conclusion, we evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.« less

Systemic neonatal candidosis: the karyotyping of Candida albicans strains isolated from neonates and health-workers.

PubMed

Ben Abdeljelil, J; Ben Saida, N; Saghrouni, F; Fathallah, A; Boukadida, J; Sboui, H; Ben Said, M

2010-01-01

Candida albicans has become an important cause of nosocomial infections in neonatal intensive care units (NICUs). The aim of the present study was to compare C. albicans strains isolated from neonates (NN) suffering from systemic candidosis and from nurses in order to determine the relatedness between NN and health workers' strains. Thirty-one C. albicans strains were isolated from 18 NN admitted to the NICU of the neonatology service of Farhat Hached Hospital of Sousse, Tunisia and suffering from systemic candidosis, together with five strains recovered from nurses suffering from C. albicans onychomycosis. Two additional strains were tested, one from an adult patient who developed a systemic candidosis and the second from an adult with inguinal intertrigo. All strains were karyotyped by pulsed-field gel electrophoresis (PFGE) with a CHEF-DR II system. Analysis of PFGE patterns yielded by the 38 strains tested led to the identification of three pulsotypes that were designated I, II and III, and consisted of six chromosomal bands with a size ranging from 700 to >2500 kbp. The most widespread was the pulsotype I, which was shared by 17 NN and the five nurses' strains. The identity between NN and nurses' strains is very suggestive of a nosocomial acquisition from health-workers.