Two-component dressed-bag model for NN interaction: deuteron structure and phase shifts up to 1 GeV

International Nuclear Information System (INIS)

Kukulin, V.I.; Obukhovsky, I.T.; Pomerantsev, V.N.; Faessler, A.

2002-01-01

A two-component model is developed for the intermediate-range NN interaction based on a new mechanism with an intermediate symmetric six-quark bag 'dressed' by σ and other fields. To calculate the transition amplitude, the microscopic six-quark shell-model in combination with the 3 P 0 -quark pion production mechanism is used. As a result, an effective energy-dependent NN interaction is constructed. The new quark-meson model for the NN interaction has been demonstrated to result in a new type of NN tensor force at intermediate ranges, which is crucially important for the treatment of tensor mixing at intermediate energies. The suggested model is able to describe NN phase shifts in a broad energy range from low energy up to 1 GeV, and the deuteron structure. The generalization of the model results in new spin-orbit 2N and 3N forces and new meson-exchange currents induced by intermediate dressed bag components, and also in the enhancement of a collective σ-field in nuclei. (author)

Important configurations in six-quark N-N states. II. Current quark model

International Nuclear Information System (INIS)

Stancu, F.; Wilets, L.

1989-01-01

Quark basis states constructed from molecular-type orbitals were shown previously to be more convenient to use than cluster model states for N-N processes. The usual cluster model representation omits configurations which emerge naturally in a molecular basis which contains the same number of spatial functions. The importance of the omitted states was demonstrated for a constituent quark model. The present work extends the study to the prototypical current quark model, namely the MIT bag. In order to test the expansion for short-range N-N interactions, the eigenstates and eigenenergies of six quarks in a spherical bag, including one-gluon exchange, are calculated. The lowest eigenenergies are lowered significantly with respect to the usual cluster model. This reaffirms the importance of dynamics for obtaining the needed short-range repulsion

Present status of the theory of πNN systems

International Nuclear Information System (INIS)

Mizutani, T.

1987-01-01

In the present discussion the existing data are compared with various model calculations within the πNN theory to assess the appropriateness of the latter. Globally the models in their present form reproduce the data quite well in view of the number of channels in which the comparison was made. From the quantitative point of view the models must be upgraded in several different ingredients. First, it is the parameters of the NN heavy meson exchange potentials when combined with the explicit pion and nucleon isobar degrees of freedom. A right choice of their values will lead to a better account of such observables like the beam asymmetries and vector analyzing power in a number of channels like NN in equilibrium πd, NN → πNN, etc. The next one may be the model for the Δ resonance (or the πN P 33 off-shell amplitude). A simple monopole or dipole form factor for the πNΔ vertex had difficulty in reproducing the major NN partial wave phase parameters. In view of the fact that dσ/dΩ for the deuteron photodisintegration in the Δ resonance region cannot be explained at all by the coupled NN-NΔ model, one should seriously consider a better model for the Δ. 39 refs., 10 figs

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

Directory of Open Access Journals (Sweden)

Melike Bildirici

2014-01-01

Full Text Available The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100. Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.

On the theory of coupled πNN-NN systems

International Nuclear Information System (INIS)

Machavariani, A.I.

1982-01-01

On the basis of the deuteron and δ asobar as a one-particle state, a version of relativistic equations for coupled πNN and NN systems is obtained. It is demonstrated that, if one neglects the non-pole term of the pion-nucleon Green function in the (3.3) resonance region, the three-body equations reduce to a set of equations for the two-body amplitudes of transitions between the πd, NN and Nδ channels

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

Science.gov (United States)

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.

Determination of nn scattering length from data on nn final state interaction in nd-breakup reaction

International Nuclear Information System (INIS)

Konobeevski, E.S.; Mordovskoy, M.V.; Sergeev, V.A.; Potashev, S.I.; Zuev, S.V.

2006-01-01

Full text: An experiment is proposed for the high-precision determination of the neutron-neutron scattering length investigating the nn final state interaction in the nd breakup reaction. The singlet pp and nn scattering lengths are very sensitive probes of the NN-interaction, and their difference is a direct measure of charge-symmetry breaking (CSB) of the nuclear force. However CSB is a small effect, and accurate values of the scattering lengths are needed for a theoretical analysis. The proton-proton scattering length is well known from pp-scattering data (a pp = -17.3± 0.4 fm), and its uncertainty is mainly due to a model-dependent procedure of removing Coulomb effects. The neutron-neutron scattering length is determined from the following processes n+d→p+n+n, π - + d → γ +n+n, d+d→ 2 He+n+n by investigating the kinematic region of the nn final-state interaction (FSI) where two neutrons fly with low relative energy. The results obtained by now are characterized by a significant uncertainty in values of a nn ; they are grouped near -16 and -19 fm [1,2], so even the sign of the difference a nn - a pp is uncertain. In this experiment neutron-neutron scattering length is determined by measuring the yield of the nd breakup reaction as a function of the relative energy ε nn =(E 1 +E 2 -2(E 1 E 2 ) 1/2 cosθ)/2 of two neutrons in the FSI region (two neutrons fly in a narrow angular cone) where nn-interaction is strongly revealed. The theory of reactions in 3N system predicts the ε nn dependence of the FSI cross section being sensitive to the value of a nn . The measurements will be made using the neutron channel RADEX at Moscow meson factory of the Institute for Nuclear Research. The momenta and angles of the two emitted neutrons and the energy of the proton will be measured for each breakup event. The measured dependence of the reaction yield on the relative energy of the two neutrons will be compared to results of the Monte Carlo simulation that includes

Consistency requirements on Δ contributions to the NN potential

International Nuclear Information System (INIS)

Rinat, A.S.

1982-04-01

We discuss theories leading to intermediate state NΔ and ΔΔ contributions to Vsub(NN). We focus on the customary addition of Lsub(ΔNπ)' to Lsub(πNN)' in a conventional field theory and argue that overcounting of contributions to tsub(πN) and Vsub(NN) will be the rule. We then discuss the cloudy bag model where a similar interaction naturally arises and which leads to a consistent theory. (author)

Construction of high-quality NN potential models

International Nuclear Information System (INIS)

Stoks, V.G.J.; Klomp, R.A.M.; Terheggen, C.P.F.; de Swart, J.J.

1994-01-01

We present an updated version (Nijm93) of the Nijmegen soft-core potential, which gives a much better description of the np data than the older version (Nijm78). The χ 2 per datum is 1.87. The configuration-space and momentum-space versions of this potential are exactly equivalent, a unique feature among meson-theoretical potentials. We also present three new NN potential models: a nonlocal Reid-like Nijmegen potential (Nijm I), a local version (Nijm II), and an updated regularized version (Reid 93) of the Reid soft-core potential. These three potentials all have a nearly optimal χ 2 per datum and can therefore be considered as alternative partial-wave analyses. All potentials contain the proper charge-dependent one-pion-exchange tail

Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Directory of Open Access Journals (Sweden)

Sharif Uddin

2016-01-01

Full Text Available An enhanced k-nearest neighbor (k-NN classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional k-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed k-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced k-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, k.

π-exchange NN interaction model with overlapping nucleon form factors

International Nuclear Information System (INIS)

Bagnoud, X.

1986-01-01

The nucleon-nucleon (NN) interaction model includes a π-exchange and takes into account the first excited state Δ(1232) of the nucleon. It is supplemented by a short-range repulsion which has been derived from the nucleon form factor (rms radius b/sub f/) combined with the three-quark wave function (rms radius b/sub q/). The optimization of the model on empirical scattering phase shifts below 300 MeV gives, for a minimum chi 2 , the root-mean-square radii b/sub f/ = b/sub q/ = 0.51 fm and a coupling constant G/sub π/ 2 /4π = 13

Three-body ΛNN force due to Λ-Σ coupling

International Nuclear Information System (INIS)

Myint, Khin Swe; Akaishi, Yoshinori

2003-01-01

The ΛNN three - body force due to coherent Λ - Σ Coupling effect was derived from realistic Nijmegen model D potential. Repulsive and attractive three - body ΛNN forces were reconcilably accounted. For 5 He, within one - channel description, ΛNN force is largely repulsive and its origin comes from Pauli forbidden terms. Within two - channel description, attractive Pauli allowed terms exist and resulting three - body force is always attractive. Large attractive ΛNN force effect due to coherent Λ - Σ coupling effect is predicted in neutron - rich nuclei. The attractive coherent Λ - Σ coupling effect is largely enhanced at high density neutron matter. The attractive three - body ΛNN force effect is essential dynamics of Λ - Σ coupling while the repulsive Nogami three - body effect arises from Pauli forbidden diagrams. (Y. Kazumata)

NN → NN π: the new frontier in nucleon-nucleon interactions

International Nuclear Information System (INIS)

Silbar, R.R.

1986-01-01

The torch in nucleon-nucleon scattering has been passed to experimental and theoretical studies of pion production. Comparing two unitary models shows that most of the structures predicted for spin observables in NN → NNπ are model independent and roughly in agreement with the data. The contribution of rho- exchange is small, indicating the reaction is largely ''peripheral''. The energy dependence of these isobar models is smooth. The largely unstudied reactions producing neutral and negatively-charged pions show richer structure than positively-charged pion production. 6 refs

Further investigations of the NN interaction in the Skyrme model

International Nuclear Information System (INIS)

Kaelbermann, G.; Eisenberg, J.M.

1989-01-01

We examine the influence of the coupling to NΔ and ΔΔ degrees of freedom for the NN interaction as derived in the Skyrme model, carrying out an extensive search for parameters in the basic Lagrangian that will yield both reasonable single-baryon results and appreciable attraction. Separately the free one-body skyrmeon solution and an improved two-body solution are inserted in the product ansatz for the two-body system both with and without time-dependent dynamical terms. No appreciable central attraction between nucleons is found with either of these approaches. (author)

A unitary approach to the coupling between the NN and πNN channels

International Nuclear Information System (INIS)

Blankleider, B.

1980-11-01

Some basic properties of the πNN system, in particular its coupling to the NN channel, are investigated. A set of linear integral equations that couple the N-N to the π-d channel, and satisfy two- and three-body unitarity is derived. By including the π-N amplitude in the P 11 channel, and retaining certain disconnected diagrams, it is found that the propagators for the nucleons, and form factors for the vertices, become dressed without changing the basic structure of the equations. For the numerical solution relativistic kinematics for the pion and non-relativistic kinematics for the nucleons are used. There is uncertainty about the importance of real pion absorption in the π-d elastic scattering reaction. Although the effect of absorption can be very large, its influence is cancelled to a large extent by the further inclusion of P 11 rescattering. The inclusion of absorption signnificantly lowers the dips in the π-d differential cross sections at higher energies. The model is able to reproduce the sole experimental value of the tensor polarization t 20 at 180 deg. so far available. Numerical results for the reaction NN→πd are in excellent agreement with the differential cross sections at all but the very high energies

Projection of the six-quark wave function onto the NN channel and the problem of the repulsive core in the NN interaction

International Nuclear Information System (INIS)

Kusainov, A.M.; Neudatchin, V.G.; Obukhovsky, I.T.

1991-01-01

A modification of the resonating-group method (RGM) is proposed which includes the multiquark shell-model configurations in the nucleon overlap region. The instanton, gluon, and π,σ exchange is taken into account, the interaction constants being consistent with the baryon spectrum. This enables one to cover a wide interval of NN scattering energies up to E lab =2 GeV. The projection of the six-quark wave function onto the NN and other baryon channels is discussed in detail in our approach and in other RGM versions as well, and in this context the problem of repulsive core in the NN forces is discussed

A consistent meson-theoretic description of the NN-interaction

International Nuclear Information System (INIS)

Machleidt, R.

1985-01-01

In this paper, the meson-theory of the NN-interaction is performed consistently. All irreducible diagrams up to a total exchanged mass of about 1 GeV (i. e. up to the cutoff region) are taken into account. These diagrams contain in particular an explicit field theoretic model for the 2π-exchange taking into account virtual Δ-excitation and direct π π-interaction. This part of the model agrees quantitatively with results obtained from dispersion theory which in turn are based on the analysis of πN- and π π-scattering data. A detailed description of the lower partial wave phase-shifts of NN-scattering requires the introduction of irreducible diagrams containing also heavy boson exchange, in particular the combination of π and rho. In the framework of this consistent meson theory an accurate description of the NN-scattering data below 300 MeV laboratory energy as well as the deuteron data is achieved; the numerical results are superior to those of simplified boson exchange models

A consistent analysis of (p,p`) and (n,n`) reactions using the Feshbach-Kerman-Koonin model

Energy Technology Data Exchange (ETDEWEB)

Yoshioka, S.; Watanabe, Y.; Harada, M. [Kyushu Univ., Fukuoka (Japan)] [and others

1997-03-01

Double-differential proton emission cross sections were measured for proton-induced reactions on several medium-heavy nuclei ({sup 54,56}Fe, {sup 60}Ni, {sup 90}Zr, and {sup 93}Nb) at two incident energies of 14.1 and 26 MeV. The (p,p`) data for {sup 56}Fe and {sup 93}Nb were compared with available data of (n,n`) scattering for the same target nuclei and incident energies, and both data were analyzed using the Feshbach-Kerman-Koonin model to extract the strength V{sub 0} of the effective N-N interaction which is the only free parameter used in multistep direct calculations. (author)

Dihadron Azimuthal Correlations in p-p Collisions at sNN=7 TeV and p-Pb Collisions at sNN=5.02 TeV

Directory of Open Access Journals (Sweden)

Ting Bai

2015-01-01

Full Text Available The dihadron azimuthal correlations in p-p collisions at sNN=7 TeV and p-Pb collisions at sNN=5.02 TeV are investigated in the framework of a multisource thermal model. The model can approximately describe the experimental results measured in the Large Hadron Collider. We find the px amplitude of the source is magnified and the source translates along the direction.

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Directory of Open Access Journals (Sweden)

J. C. Ochoa-Rivera

2002-01-01

Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..

Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2008-01-01

In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (author)

ABOUT PROBABILITY OF RESEARCH OF THE NN Ser SPECTRUM BY MODEL ATMOSPHERES METHOD

OpenAIRE

Sakhibullin, N. A.; Shimansky, V. V.

2017-01-01

The spectrum of close binary system NN Ser is investigated by a models atmospheres method. It is show that the atmosphere near the centrum of a hot spot on surface of red dwarf has powerful chromospheres, arising from heating in Laiman continua. Four models of binary system with various of parameters are constructed and their theoretical spectra are obtained. Temperature of white dwarf Tef = 62000 K, radius of the red dwarf RT = 0.20139 and angle inclination of system i = 82“ are determined. ...

Hybrid2 - The hybrid power system simulation model

Energy Technology Data Exchange (ETDEWEB)

Baring-Gould, E.I.; Green, H.J.; Dijk, V.A.P. van [National Renewable Energy Lab., Golden, CO (United States); Manwell, J.F. [Univ. of Massachusetts, Amherst, MA (United States)

1996-12-31

There is a large-scale need and desire for energy in remote communities, especially in the developing world; however the lack of a user friendly, flexible performance prediction model for hybrid power systems incorporating renewables hindered the analysis of hybrids as options to conventional solutions. A user friendly model was needed with the versatility to simulate the many system locations, widely varying hardware configurations, and differing control options for potential hybrid power systems. To meet these ends, researchers from the National Renewable Energy Laboratory (NREL) and the University of Massachusetts (UMass) developed the Hybrid2 software. This paper provides an overview of the capabilities, features, and functionality of the Hybrid2 code, discusses its validation and future plans. Model availability and technical support provided to Hybrid2 users are also discussed. 12 refs., 3 figs., 4 tabs.

Formation of q bar q resonances in the bar NN system

International Nuclear Information System (INIS)

Ivanov, N.Ya.

1995-01-01

The formation of q bar q resonances lying on the leading Regge trajectories in the bar NN system is studied in the quark-gluon string model. The model predicts strong suppression of the decays of q bar q states into bar NN pairs in relation to two-meson modes. The author's analysis shows that the contributions of the resonances f 4 (2050) (I G J PC = 0 + 4 ++ ), ρ 5 (2240) (I G J PC = 1 + 5 -- ), and f 6 (2510) (I G J PC = 0 + 6 ++ ) to the processes of two-meson bar NN annihilation (bar pp → ππ, bar KK, hor-ellipsis) are about 1% of the corresponding experimental integrated cross sections. 30 refs., 2 figs., 1 tab

Classification in medical images using adaptive metric k-NN

Science.gov (United States)

Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

2010-03-01

The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

Deep attractive NN potential as a potential for the N(1440)-N system

International Nuclear Information System (INIS)

Glozman, L.Y.; Kukulin, V.I.; Pomerantsev, V.N.

1992-01-01

This work demonstrates that the model of a deep attractive NN potential (the Moscow NN potential) with a deep lying extra state may be interpreted as a potential in the N(1440)-N system. Under such an interpretation, the deep-lying level forbidden for the NN system describes the N(1440)-N component in deuteron. This conclusion is used to obtain the momentum distribution for the N(1440)-N component in deuteron

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

NΔ-NN interaction in the pionic disintegration of the deuteron

International Nuclear Information System (INIS)

Tanabe, H.; Ohta, K.

1987-07-01

The cross sections for the pionic disintegration of the deuteron in the Δ-resonance region are calculated based on a unitary three-body model. The NΔ-NN transition potential is constructed from the πN P 11 and P 33 scattering amplitudes extrapolated to the off-shell region, and from the πNN three-body propagator. The idea of the two-potential model for the P 11 wave is extended to the P 33 wave. The parameters of the model are deduced from the fits to the πN scattering phase shifts. It is found that the off-shell P 11 and P 33 scattering amplitudes behave quite similarly to the monopole form factor with a cut-off momentum Λ = 600 MeV/c as obtained earlier in the perturbation model by Gibbs, Gibson, and Stephenson. It is also found that the backward-propagating-pion component of the πNN propagator, which is often ignored in three-body calculations, is crucial to reproduce the magnitude of the total cross section. The three-body calculation is compared to the perturbation calculations. The second-order perturbation gives the results which closely approximate the full-order three-body calculation. (author)

Meson-exchange Hamiltonian for NN scattering and isobar-nucleus dynamics

International Nuclear Information System (INIS)

Lee, T.S.H.

1983-01-01

We have constructed a meson-exchange Hamiltonian for π, N, Δ and N* for NN scattering up to 2 GeV. The model gives good descriptions of the Arndt phase-shifts up to 1 GeV in both the T = 0 and T = 1 channels. The calculated total cross sections sigma/sup tot/, Δsigma/sub L//sup tot/ and Δsigma/sub T//sup tot/ agree to a large extent with the data in both the magnitudes and the signs. The present calculation gives a sound starting point for future refinements. Among them, a large-scale three-body calculation could be needed to investigate the energy dependence of the effect due to NN interactions in the πNN channel. Until this effect is carefully studied, it is premature to extract information on dibaryon resonances, if they exist, from the data. Our model also gives definite predictions of np scattering. Precise np polarization measurements at higher energy > 0.6 GeV are needed to have a complete test of our model. Finally, the present model Hamiltonian can be used to carry our many-body calculations

GeNN: a code generation framework for accelerated brain simulations

Science.gov (United States)

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

New approach to the theory of coupled πNN-NN system. III. A three-body limit

International Nuclear Information System (INIS)

Avishai, Y.; Mizutani, T.

1980-01-01

In the limit where the pion is restricted to be emitted only by the nucleon that first absorbed it, it is shown that the equations previously developed to describe the couple πNN (πd) - NN system reduce to conventional three-body equations. Specifically, it is found in this limit that the input πN p 11 amplitude which, put on-shell, is directly related to the experimental phase shift, contrary to the original equations where the direct (dressed) nucleon pole term and the non-pole part of this partial wave enter separately. The present study clarifies the limitation of pure three-body approach to the πNN-NN problems as well as suggests a rare opportunity of observing a possible resonance behavior in the non-pole part of the πN P 11 amplitude through πd experiments

Prediction of the electron redundant SinNn fullerenes

Science.gov (United States)

Yang, Huihui; Song, Yan; Zhang, Yan; Chen, Hongshan

2018-05-01

The stabilities and electronic structures of SimAln-mNn and SinNn (n = 16, 20, m = 12 and n = 24, m = 16) fullerene-like cages have been investigated using density functional method B3LYP and the second-order perturbation theory MP2. The results show that the SimAln-mNn and SinNn fullerenes are more stable than the AlN counterparts. Comparing with the corresponding AlnNn cages, one silicon atom in each Si2N2 square protrudes and the excess electrons reside as lone pair electrons at the outside of the protrudent Si atoms. Analyses on the electronic structures suggest that the Sisbnd N bonds are covalent bonding with strong polarity. The ELF (electron localization function) shows large electron pair probability between Si and N atoms. The orbital interactions between Si and N are stronger than that between Al and N atoms; the overlap integral is 0.40 per Sisbnd N bond in SinNn and 0.34 per Alsbnd N bond in AlnNn. The AIM (atoms in molecule) charges on the Al atoms in AlnNn and SimAln-mNn are 2.37 and 2.40. The charges on the in-plane and protrudent Si atoms are about 2.88 and 1.50 respectively. Considering the large local dipole moments around the protrudent Si atoms, the electrostatic interactions are also favorable to the SiN cages.

What quark theory gives for the potential description of the parity violation in NN interactions

International Nuclear Information System (INIS)

Dubovik, V.M.; Zenkin, S.V.

1982-01-01

The constants of the parity violating (PV) πNN, rhoNN and #betta#NN interactions are calculated in the framework of quark picture based on the standard SU(2)sub(L)xU(1) electroweak model with account for the QCD corrections. The constants are close to the well-known ''best values'', which provide a successful fit to the low-energy PV experimental data

Neural-network hybrid control for antilock braking systems.

Science.gov (United States)

Lin, Chih-Min; Hsu, C F

2003-01-01

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Science.gov (United States)

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

Comparative Study of Bancruptcy Prediction Models

Directory of Open Access Journals (Sweden)

Isye Arieshanti

2013-09-01

Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

Modified GMDH-NN algorithm and its application for global sensitivity analysis

International Nuclear Information System (INIS)

Song, Shufang; Wang, Lu

2017-01-01

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages. - Highlights: • The GMDH-NN is improved to construct the explicit polynomial model of optimal complexity by self-organization. • The paper aims at combining improved GMDH-NN with HDMR expansions and using it to compute Sobol' indices directly. • The method can be applied in uniform, normal and exponential distribution by using suitable orthogonal polynomials. • Engineering examples, e.g., electronic circuit models can be solved by the presented method.

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

Science.gov (United States)

Li, Weide; Kong, Demeng; Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

Compositional Modelling of Stochastic Hybrid Systems

NARCIS (Netherlands)

Strubbe, S.N.

2005-01-01

In this thesis we present a modelling framework for compositional modelling of stochastic hybrid systems. Hybrid systems consist of a combination of continuous and discrete dynamics. The state space of a hybrid system is hybrid in the sense that it consists of a continuous component and a discrete

Medium energy inelastic proton-nucleus scattering with spin dependent NN interaction

International Nuclear Information System (INIS)

Ahmad, I.; Auger, J.P.

1981-12-01

The previously proposed effective profile expansion method for the Glauber multiple scattering model calculation has been extended to the case of proton-nucleus inelastic scattering with spin dependent NN interaction. Using the method which turns out to be computationally simple and of relatively wider applicability, a study of sensitivity of proton-nucleus inelastic scattering calculation to the sometimes neglected momentum transfer dependence of the NN scattering amplitude has been made. We find that the calculated polarization is particularly sensitive in this respect. (author)

New approach to the theory of coupled πNN-NN systems. 4. Completion of formal development

International Nuclear Information System (INIS)

Avishai, Y.; Mizutani, T.

1980-05-01

We complete our discussion pertaining to the coupled πNN-NN system in succession of our previous works and approach the problem in two independent ways. The first one starts from a Hamiltonian formalism and coupled Schroedinger equations whereas the second one employs an off-mass-shell relativistic theory of classifying perturbation diagrams. Both ways lead to connected equations among transition operators in which πNN vertices as well as nucleon propagators are completely dressed and renormalized. Furthermore, the physical amplitudes obey two and three body unitarity relations. The resultant equations form a sound theoretical basis for subsequent numerical calculations leading to the evaluation of physical observables in the reactions π+d→π+d, π+d→N+N and N+N→N+N

Hybrid method to predict the resonant frequencies and to characterise dual band proximity coupled microstrip antennas

Science.gov (United States)

Varma, Ruchi; Ghosh, Jayanta

2018-06-01

A new hybrid technique, which is a combination of neural network (NN) and support vector machine, is proposed for designing of different slotted dual band proximity coupled microstrip antennas. Slots on the patch are employed to produce the second resonance along with size reduction. The proposed hybrid model provides flexibility to design the dual band antennas in the frequency range from 1 to 6 GHz. This includes DCS (1.71-1.88 GHz), PCS (1.88-1.99 GHz), UMTS (1.92-2.17 GHz), LTE2300 (2.3-2.4 GHz), Bluetooth (2.4-2.485 GHz), WiMAX (3.3-3.7 GHz), and WLAN (5.15-5.35 GHz, 5.725-5.825 GHz) bands applications. Also, the comparative study of this proposed technique is done with the existing methods like knowledge based NN and support vector machine. The proposed method is found to be more accurate in terms of % error and root mean square % error and the results are in good accord with the measured values.

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

Directory of Open Access Journals (Sweden)

Jingli Yang

2016-12-01

Full Text Available The k-nearest neighbour (kNN rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

Science.gov (United States)

Yang, Jingli; Sun, Zhen; Chen, Yinsheng

2016-01-01

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

Folding model analysis of Λ binding energies and three-body ΛNN force

International Nuclear Information System (INIS)

Mian, M.; Rahman Khan, M.Z.

1988-02-01

Working within the framework of the folding model, we analyze the Λ binding energy data of light hypernuclei with effective two-body ΛN plus three-body ΛNN interaction. The two-body density for the core nucleus required for evaluating the three-body force contribution is obtained in terms of the centre of mass pair correlation. It is found that except for Λ 5 He the data are fairly well explained. The three-body force seems to account for the density dependence of the effective two-body ΛN interaction proposed earlier. (author). 13 refs, 2 tabs

Travelling Waves in Hybrid Chemotaxis Models

KAUST Repository

Franz, Benjamin

2013-12-18

Hybrid models of chemotaxis combine agent-based models of cells with partial differential equation models of extracellular chemical signals. In this paper, travelling wave properties of hybrid models of bacterial chemotaxis are investigated. Bacteria are modelled using an agent-based (individual-based) approach with internal dynamics describing signal transduction. In addition to the chemotactic behaviour of the bacteria, the individual-based model also includes cell proliferation and death. Cells consume the extracellular nutrient field (chemoattractant), which is modelled using a partial differential equation. Mesoscopic and macroscopic equations representing the behaviour of the hybrid model are derived and the existence of travelling wave solutions for these models is established. It is shown that cell proliferation is necessary for the existence of non-transient (stationary) travelling waves in hybrid models. Additionally, a numerical comparison between the wave speeds of the continuum models and the hybrid models shows good agreement in the case of weak chemotaxis and qualitative agreement for the strong chemotaxis case. In the case of slow cell adaptation, we detect oscillating behaviour of the wave, which cannot be explained by mean-field approximations. © 2013 Society for Mathematical Biology.

A hybrid mammalian cell cycle model

Directory of Open Access Journals (Sweden)

Vincent Noël

2013-08-01

Full Text Available Hybrid modeling provides an effective solution to cope with multiple time scales dynamics in systems biology. Among the applications of this method, one of the most important is the cell cycle regulation. The machinery of the cell cycle, leading to cell division and proliferation, combines slow growth, spatio-temporal re-organisation of the cell, and rapid changes of regulatory proteins concentrations induced by post-translational modifications. The advancement through the cell cycle comprises a well defined sequence of stages, separated by checkpoint transitions. The combination of continuous and discrete changes justifies hybrid modelling approaches to cell cycle dynamics. We present a piecewise-smooth version of a mammalian cell cycle model, obtained by hybridization from a smooth biochemical model. The approximate hybridization scheme, leading to simplified reaction rates and binary event location functions, is based on learning from a training set of trajectories of the smooth model. We discuss several learning strategies for the parameters of the hybrid model.

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China

Directory of Open Access Journals (Sweden)

Weide Li

2017-01-01

Full Text Available Air pollution in China is becoming more serious especially for the particular matter (PM because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM, in which the parameters c and g are optimized by flower pollination algorithm (FPA. Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

Write up of the codes for microscopic models of NN and NA scattering

International Nuclear Information System (INIS)

Amos, K.

1998-01-01

This report documents the essential details of the NN and NA computer programs that culminate in the prediction of elastic and inelastic nucleon scattering observables form optical potentials generated by full folding and effective NN interaction within the nuclear medium. That same (energy and density dependent) effective interaction is used as the transition operator in the distorted wave approximation (DWA) for inelastic (and charge exchange) nucleon scattering from nuclei. The report consists of four sections: 1) general remarks and program locations, 2) the t- and g-matrix codes and how to use them, 3) the effective interaction codes and how to use them, and 4) the NA codes, DWBA97 and DWBB97 and how to use them. (author)

Comments On Clock Models In Hybrid Automata And Hybrid Control Systems

Directory of Open Access Journals (Sweden)

Virginia Ecaterina OLTEAN

2001-12-01

Full Text Available Hybrid systems have received a lot of attention in the past decade and a number of different models have been proposed in order to establish mathematical framework that is able to handle both continuous and discrete aspects. This contribution is focused on two models: hybrid automata and hybrid control systems with continuous-discrete interface and the importance of clock models is emphasized. Simple and relevant examples, some taken from the literature, accompany the presentation.

Analysis of NN amplitudes up to 2.5 GeV: an optical model and geometric interpretation

International Nuclear Information System (INIS)

Geramb, H.V. von; Universitaet Hamburg, Hamburg,; Amos, K.A.; Labes, H.; Sander, M.

1998-01-01

We analyse the SM97 partial wave amplitudes for nucleon-nucleon (NN) scattering to 2.5 GeV, in which resonance and meson production effects are evident for energies above pion production threshold. Our analyses are based upon boson exchange or quantum inversion potentials with which the sub-threshold data are fit perfectly. Above 300 MeV they are extrapolations, to which complex short ranged Gaussian potentials are added in the spirit of the optical models of nuclear physics and of diffraction models of high energy physics. The data to 2.5 GeV are all well fit. The energy dependences of these Gaussians are very smooth save for precise effects caused by the known Δ and N* resonances. With this approach, we confirm that the geometrical implications of the profile function found from diffraction scattering are pertinent in the regime 300 MeV to 2.5 GeV and that the overwhelming part of meson production comes from the QCD sector of the nucleons when they have a separation of their centres of 1 to 1.2 fm. This analysis shows that the elastic NN scattering data above 300 MeV can be understood with a local potential operator as well as has the data below 300 MeV

Spectroscopy of light nuclei with realistic NN interaction JISP

International Nuclear Information System (INIS)

Shirokov, A. M.; Vary, J. P.; Mazur, A. I.; Weber, T. A.

2008-01-01

Recent results of our systematic ab initio studies of the spectroscopy of s- and p-shell nuclei in fully microscopic large-scale (up to a few hundred million basis functions) no-core shell-model calculations are presented. A new high-quality realistic nonlocal NN interaction JISP is used. This interaction is obtained in the J-matrix inverse-scattering approach (JISP stands for the J-matrix inverse-scattering potential) and is of the form of a small-rank matrix in the oscillator basis in each of the NN partial waves, providing a very fast convergence in shell-model studies. The current purely two-body JISP model of the nucleon-nucleon interaction JISP16 provides not only an excellent description of two-nucleon data (deuteron properties and np scattering) with χ 2 /datum = 1.05 but also a better description of a wide range of observables (binding energies, spectra, rms radii, quadrupole moments, electromagnetic-transition probabilities, etc.) in all s-and p-shell nuclei than the best modern interaction models combining realistic nucleon-nucleon and three-nucleon interactions.

Can the Σ-nn system be bound?

International Nuclear Information System (INIS)

Stadler, A.; Gibson, B.F.

1994-01-01

Motivated by the Σ-hypernuclear states reported in (K - ,π ± ) experiments, we have explored the possibility that there exists a particle-stable Σ - nn bound state. For the Juelich A hyperon-nucleon, realistic-force model, our calculations yield little reason to expect a positive-parity bound state or resonance in either the J=1/2 or the J=3/2 channels

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

Science.gov (United States)

Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

2016-09-01

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

A meson-theoretical model for the πρinteraction and the πNN form factor

International Nuclear Information System (INIS)

Janssen, G.

1993-02-01

Based on the successful description of ππ interaction within the meson exchange framework we extend our model to a further meson-meson process, the πρ interaction. In comparison with ππ interaction several new aspects appear in the πρ system. Besides other points the main new aspect is due to the instability of the ρ-meson, which induces a transition of the πρ system into a 3π system. A realistic model for the πρ interaction should take into account coupling to this three-particle channel and thus requires application of the complicated three-body formalism. Having obtained the πρ T-matrix we first investigate the L JT = S 11 partial wave, i.e. the A 1 -channel. Here, ρ-exchange provides the dominant exchange contribution; however, it is definitely necessary to include the genuine A 1 -pole term. The detailed investigation of the structure of the resulting T-matrix provides an explanation for discrepancies existing in the interpretation of different experimental data sets. As a first application of our πρ interaction model we investigate the πNN vertex. We consider the meson cloud part of the vertex extension by calculating loop corrections to the pointlike vertex. It turns out that the formfactor is rather soft in our calculation (Λ πNN ≅ 1.0GeV; monopole parametrization). For this result the inclusion of the πρ interaction is quite important, because it leads to a remarkable shift in the formfactor towards lower cut-off masses. (orig.) [de

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

Science.gov (United States)

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

Statistical model calculation of fission isomer excitation functions in (n,n') and (n,γ) reactions

International Nuclear Information System (INIS)

Chatterjee, A.; Athougies, A.L.; Mehta, M.K.

1977-01-01

A statistical model developed by Britt and others (1971, 1973) to analyze isomer excitation functions in spallation type reactions like (α,2n) has been adopted in fission isomer calculations for (n,n') and (n,γ) reactions. Calculations done for 235 U(n,n')sup(238m)U and 235 U(n,γ)sup(236m)U reactions have been compared with experimental measurements. A listing of the computer program ISOMER using FORTRAN IV to calculate the isomer to prompt ratios is given. (M.G.B.)

Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

Science.gov (United States)

2016-01-01

The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

Signal peptide discrimination and cleavage site identification using SVM and NN.

Science.gov (United States)

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

NN-SITE: A remote monitoring testbed facility

International Nuclear Information System (INIS)

Kadner, S.; White, R.; Roman, W.; Sheely, K.; Puckett, J.; Ystesund, K.

1997-01-01

DOE, Aquila Technologies, LANL and SNL recently launched collaborative efforts to create a Non-Proliferation Network Systems Integration and Test (NN-Site, pronounced N-Site) facility. NN-Site will focus on wide area, local area, and local operating level network connectivity including Internet access. This facility will provide thorough and cost-effective integration, testing and development of information connectivity among diverse operating systems and network topologies prior to full-scale deployment. In concentrating on instrument interconnectivity, tamper indication, and data collection and review, NN-Site will facilitate efforts of equipment providers and system integrators in deploying systems that will meet nuclear non-proliferation and safeguards objectives. The following will discuss the objectives of ongoing remote monitoring efforts, as well as the prevalent policy concerns. An in-depth discussion of the Non-Proliferation Network Systems Integration and Test facility (NN-Site) will illuminate the role that this testbed facility can perform in meeting the objectives of remote monitoring efforts, and its potential contribution in promoting eventual acceptance of remote monitoring systems in facilities worldwide

Investigation of the charge exchange process π-d→π0nn

International Nuclear Information System (INIS)

Il-Tong Cheon.

1978-02-01

The transition rate has been calculated for the charge exchange of stopped π - in the deuteron. The present result is hω(π - d→π 0 nn)=0.695x10 -4 eV. Making use of the value hω(π - d→nn)=0.682 eV, which was previously obtained, estimated the branching ratio ω(π - d→π 0 nn)|ω(π - d→nn)=1.02x10 -4 has been estimated

Deriving simulators for hybrid Chi models

NARCIS (Netherlands)

Beek, van D.A.; Man, K.L.; Reniers, M.A.; Rooda, J.E.; Schiffelers, R.R.H.

2006-01-01

The hybrid Chi language is formalism for modeling, simulation and verification of hybrid systems. The formal semantics of hybrid Chi allows the definition of provably correct implementations for simulation, verification and realtime control. This paper discusses the principles of deriving an

Spin correlations in NN-NNπ reactions

International Nuclear Information System (INIS)

Davison, N.E.

1990-01-01

This paper reports that even after years of intensive work on the coupled NN-NNπ reactions, there are still some remarkable simple things that we do not know about the NN interactions at a few hundred MeV notably: Do dibaryon resonances exist, and if so, what are their masses and widths? How much isospin I = O interaction is there in the np channel? Why is the microscopic description of such a basic process as single pion production so elusive?

Pion, Kaon, and Proton Production in Central Pb--Pb Collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

CERN Document Server

Abelev, Betty; Adamova, Dagmar; Adare, Andrew Marshall; Aggarwal, Madan; Aglieri Rinella, Gianluca; Agocs, Andras Gabor; Agostinelli, Andrea; Aguilar Salazar, Saul; Ahammed, Zubayer; Ahmad, Arshad; Ahmad, Nazeer; Ahn, Sang Un; Akindinov, Alexander; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Almaraz Avina, Erick Jonathan; Alme, Johan; Alt, Torsten; Altini, Valerio; Altinpinar, Sedat; Altsybeev, Igor; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshauser, Harald; Arbor, Nicolas; Arcelli, Silvia; Armesto, Nestor; Arnaldi, Roberta; Aronsson, Tomas Robert; Arsene, Ionut Cristian; Arslandok, Mesut; Augustinus, Andre; Averbeck, Ralf Peter; Awes, Terry; Aysto, Juha Heikki; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bailhache, Raphaelle Marie; Bala, Renu; Baldini Ferroli, Rinaldo; Baldisseri, Alberto; Baldit, Alain; Baltasar Dos Santos Pedrosa, Fernando; Ban, Jaroslav; Baral, Rama Chandra; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Barret, Valerie; Bartke, Jerzy Gustaw; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batyunya, Boris; Baumann, Christoph Heinrich; Bearden, Ian Gardner; Beck, Hans; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bellwied, Rene; Belmont-Moreno, Ernesto; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bergognon, Anais Annick Erica; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhati, Ashok Kumar; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Bjelogrlic, Sandro; Blanco, F; Blanco, Francesco; Blau, Dmitry; Blume, Christoph; Bock, Nicolas; Boettger, Stefan; Bogdanov, Alexey; Boggild, Hans; Bogolyubsky, Mikhail; Boldizsar, Laszlo; Bombara, Marek; Book, Julian; Borel, Herve; Borissov, Alexander; Bose, Suvendu Nath; Bossu, Francesco; Botje, Michiel; Botta, Elena; Boyer, Bruno Alexandre; Braidot, Ermes; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Browning, Tyler Allen; Broz, Michal; Brun, Rene; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Busch, Oliver; Buthelezi, Edith Zinhle; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calvo Villar, Ernesto; Camerini, Paolo; Canoa Roman, Veronica; Cara Romeo, Giovanni; Carena, Francesco; Carena, Wisla; Carminati, Federico; Casanova Diaz, Amaya Ofelia; Castillo Castellanos, Javier Ernesto; Casula, Ester Anna Rita; Catanescu, Vasile; Cavicchioli, Costanza; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Chang, Beomsu; Chapeland, Sylvain; Charvet, Jean-Luc Fernand; Chattopadhyay, Sukalyan; Chattopadhyay, Subhasis; Chawla, Isha; Cherney, Michael Gerard; Cheshkov, Cvetan; Cheynis, Brigitte; Chiavassa, Emilio; Chibante Barroso, Vasco Miguel; Chinellato, David; Chochula, Peter; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Coccetti, Fabrizio; Colamaria, Fabio; Colella, Domenico; Conesa Balbastre, Gustavo; Conesa del Valle, Zaida; Constantin, Paul; Contin, Giacomo; Contreras, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Cotallo, Manuel Enrique; Crochet, Philippe; Cuautle, Eleazar; Cunqueiro, Leticia; D'Erasmo, Ginevra; Dainese, Andrea; Dalsgaard, Hans Hjersing; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Kushal; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; de Barros, Gabriel; De Caro, Annalisa; de Cataldo, Giacinto; de Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; de Rooij, Raoul Stefan; Delagrange, Hugues; Deloff, Andrzej; Demanov, Vyacheslav; Denes, Ervin; Deppman, Airton; Di Bari, Domenico; Di Giglio, Carmelo; Di Liberto, Sergio; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Dobrowolski, Tadeusz Antoni; Dominguez, Isabel; Donigus, Benjamin; Dordic, Olja; Driga, Olga; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Dutta Majumdar, AK; Dutta Majumdar, Mihir Ranjan; Elia, Domenico; Emschermann, David Philip; Engel, Heiko; Erazmus, Barbara; Erdal, Hege Austrheim; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Evans, David; Eyyubova, Gyulnara; Fabris, Daniela; Faivre, Julien; Falchieri, Davide; Fantoni, Alessandra; Fasel, Markus; Fedunov, Anatoly; Fehlker, Dominik; Feldkamp, Linus; Felea, Daniel; Feofilov, Grigory; Fernandez Tellez, Arturo; Ferretti, Alessandro; Ferretti, Roberta; Festanti, Andrea; Figiel, Jan; Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fuchs, Ulrich; Furget, Christophe; Fusco Girard, Mario; Gaardhoje, Jens Joergen; Gagliardi, Martino; Gago, Alberto; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Garabatos, Jose; Garcia-Solis, Edmundo; Garishvili, Irakli; Gerhard, Jochen; Germain, Marie; Geuna, Claudio; Gheata, Andrei George; Gheata, Mihaela; Ghidini, Bruno; Ghosh, Premomoy; Gianotti, Paola; Girard, Martin Robert; Giubellino, Paolo; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez, Ramon; Gonzalez Ferreiro, Elena; Gonzalez-Trueba, Laura Helena; Gonzalez-Zamora, Pedro; Gorbunov, Sergey; Goswami, Ankita; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Grajcarek, Robert; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoriev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grinyov, Boris; Grion, Nevio; Grosse-Oetringhaus, Jan Fiete; Grossiord, Jean-Yves; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerra Gutierrez, Cesar; Guerzoni, Barbara; Guilbaud, Maxime Rene Joseph; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Gutbrod, Hans; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hanratty, Luke David; Hansen, Alexander; Harmanova, Zuzana; Harris, John William; Hartig, Matthias; Hasegan, Dumitru; Hatzifotiadou, Despoina; Hayrapetyan, Arsen; Heckel, Stefan Thomas; Heide, Markus Ansgar; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Herrmann, Norbert; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hicks, Bernard; Hille, Per Thomas; Hippolyte, Boris; Horaguchi, Takuma; Hori, Yasuto; Hristov, Peter Zahariev; Hrivnacova, Ivana; Huang, Meidana; Humanic, Thomas; Hwang, Dae Sung; Ichou, Raphaelle; Ilkaev, Radiy; Ilkiv, Iryna; Inaba, Motoi; Incani, Elisa; Innocenti, Gian Michele; Ippolitov, Mikhail; Irfan, Muhammad; Ivan, Cristian George; Ivanov, Andrey; Ivanov, Marian; Ivanov, Vladimir; Ivanytskyi, Oleksii; Jacobs, Peter; Janik, Malgorzata Anna; Janik, Rudolf; Jayarathna, Sandun; Jena, Satyajit; Jha, Deeptanshu Manu; Jimenez Bustamante, Raul Tonatiuh; Jirden, Lennart; Jones, Peter Graham; Jung, Hyung Taik; Jusko, Anton; Kakoyan, Vanik; Kalcher, Sebastian; Kalinak, Peter; Kalliokoski, Tuomo Esa Aukusti; Kalweit, Alexander Philipp; Kang, Ju Hwan; Kaplin, Vladimir; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karpechev, Evgeny; Kazantsev, Andrey; Kebschull, Udo Wolfgang; Keidel, Ralf; Khan, Mohisin Mohammed; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Beomkyu; Kim, Dong Jo; Kim, Do Won; Kim, Jin Sook; Kim, Minwoo; Kim, Mimae; Kim, Se Yong; Kim, Seon Hee; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Klay, Jennifer Lynn; Klein, Jochen; Klein-Bosing, Christian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Koch, Kathrin; Kohler, Markus; Kollegger, Thorsten; Kolojvari, Anatoly; Kondratiev, Valery; Kondratyeva, Natalia; Konevskih, Artem; Korneev, Andrey; Kour, Ravjeet; Kowalski, Marek; Kox, Serge; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kramer, Frederick; Kraus, Ingrid Christine; Krawutschke, Tobias; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Krus, Miroslav; Kryshen, Evgeny; Krzewicki, Mikolaj; Kucheriaev, Yury; Kugathasan, Thanushan; Kuhn, Christian Claude; Kuijer, Paul; Kulakov, Igor; Kumar, Jitendra; Kurashvili, Podist; Kurepin, A; Kurepin, AB; Kuryakin, Alexey; Kushpil, Svetlana; Kushpil, Vasily; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron de Guevara, Pedro; Lakomov, Igor; Langoy, Rune; Lara, Camilo Ernesto; Lardeux, Antoine Xavier; Lazzeroni, Cristina; Le Bornec, Yves; Lea, Ramona; Lechman, Mateusz; Lee, Graham Richard; Lee, Ki Sang; Lee, Sung Chul; Lefevre, Frederic; Lehnert, Joerg Walter; Leistam, Lars; Lemmon, Roy Crawford; Lenhardt, Matthieu Laurent; Lenti, Vito; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Lien, Jorgen; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Liu, Lijiao; Loggins, Vera; Loginov, Vitaly; Lohn, Stefan Bernhard; Lohner, Daniel; Loizides, Constantinos; Loo, Kai Krister; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lovhoiden, Gunnar; Lu, Xianguo; Luettig, Philipp; Lunardon, Marcello; Luo, Jiebin; Luparello, Grazia; Luquin, Lionel; Luzzi, Cinzia; Ma, Rongrong; Maevskaya, Alla; Mager, Magnus; Mahapatra, Durga Prasad; Maire, Antonin; Mal'Kevich, Dmitry; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Ludmila; Malzacher, Peter; Mamonov, Alexander; Manceau, Loic Henri Antoine; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Marin, Ana Maria; Marin Tobon, Cesar Augusto; Markert, Christina; Martashvili, Irakli; Martinengo, Paolo; Martinez, Mario Ivan; Martinez Davalos, Arnulfo; Martinez Garcia, Gines; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matthews, Zoe Louise; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel; Mazzoni, Alessandra Maria; Meddi, Franco; Menchaca-Rocha, Arturo Alejandro; Mercado Perez, Jorge; Meres, Michal; Miake, Yasuo; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz; Mitu, Ciprian Mihai; Mlynarz, Jocelyn; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Monteno, Marco; Montes, Esther; Moon, Taebong; Morando, Maurizio; Moreira De Godoy, Denise Aparecida; Moretto, Sandra; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhuri, Sanjib; Mukherjee, Maitreyee; Muller, Hans; Munhoz, Marcelo; Musa, Luciano; Musso, Alfredo; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Nattrass, Christine; Nicassio, Maria; Di Giglio, Carmelo; Naumov, Nikolay; Navin, Sparsh; Nayak, Tapan Kumar; Nazarenko, Sergey; Nazarov, Gleb; Nedosekin, Alexander; Nicassio, Maria; Niculescu, Mihai; Nielsen, Borge Svane; Niida, Takafumi; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Nilsen, Bjorn Steven; Nilsson, Mads Stormo; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Novitzky, Norbert; Nyanin, Alexandre; Nyatha, Anitha; Nygaard, Casper; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Oleniacz, Janusz; Oppedisano, Chiara; Ortona, Giacomo; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Pachmayer, Yvonne Chiara; Pachr, Milos; Padilla, Fatima; Pagano, Paola; Paic, Guy; Painke, Florian; Pajares, Carlos; Pal, Susanta Kumar; Palaha, Arvinder Singh; Palmeri, Armando; Papikyan, Vardanush; Pappalardo, Giuseppe; Park, Woo Jin; Passfeld, Annika; Patalakha, Dmitri Ivanovich; Paticchio, Vincenzo; Pavlinov, Alexei; Pawlak, Tomasz Jan; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitri; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Perini, Diego; Perrino, Davide; Peryt, Wiktor Stanislaw; Pesci, Alessandro; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petran, Michal; Petris, Mariana; Petrov, Plamen Rumenov; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Piccotti, Anna; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Pitz, Nora; Piuz, Francois; Piyarathna, Danthasinghe; Planinic, Mirko; Ploskon, Mateusz Andrzej; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polichtchouk, Boris; Pop, Amalia; Porteboeuf-Houssais, Sarah; Pospisil, Vladimir; Potukuchi, Baba; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puchagin, Sergey; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Pulvirenti, Alberto; Punin, Valery; Putis, Marian; Putschke, Jorn Henning; Quercigh, Emanuele; Qvigstad, Henrik; Rachevski, Alexandre; Rademakers, Alphonse; Raiha, Tomi Samuli; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Ramirez Reyes, Abdiel; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Real, Jean-Sebastien; Redlich, Krzysztof; Rehman, Attiq Ur; Reichelt, Patrick; Reicher, Martijn; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riccati, Lodovico; Ricci, Renato Angelo; Richert, Tuva; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Rodrigues Fernandes Rabacal, Bartolomeu; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roed, Ketil; Rohr, David; Rohrich, Dieter; Romita, Rosa; Ronchetti, Federico; Rosnet, Philippe; Rossegger, Stefan; Rossi, Andrea; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Rybicki, Andrzej; Sadovsky, Sergey; Safarik, Karel; Sahoo, Raghunath; Sahu, Pradip Kumar; Saini, Jogender; Sakaguchi, Hiroaki; Sakai, Shingo; Sakata, Dosatsu; Salgado, Carlos Albert; Salzwedel, Jai; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sano, Satoshi; Santo, Rainer; Santoro, Romualdo; Sarkamo, Juho Jaako; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schreiner, Steffen; Schuchmann, Simone; Schukraft, Jurgen; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Patrick Aaron; Scott, Rebecca; Segato, Gianfranco; Selyuzhenkov, Ilya; Senyukov, Serhiy; Seo, Jeewon; Serci, Sergio; Serradilla, Eulogio; Sevcenco, Adrian; Shabetai, Alexandre; Shabratova, Galina; Shahoyan, Ruben; Sharma, Natasha; Sharma, Satish; Shigaki, Kenta; Shimomura, Maya; Shtejer, Katherin; Sibiriak, Yury; Siciliano, Melinda; Sicking, Eva; Siddhanta, Sabyasachi; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Skjerdal, Kyrre; Smakal, Radek; Smirnov, Nikolai; Snellings, Raimond; Sogaard, Carsten; Soltz, Ron Ariel; Son, Hyungsuk; Song, Jihye; Song, Myunggeun; Soos, Csaba; Soramel, Francesca; Sputowska, Iwona; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stefanek, Grzegorz; Stefanini, Giorgio; Steinpreis, Matthew; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Stolpovskiy, Mikhail; Strabykin, Kirill; Strmen, Peter; Suaide, Alexandre Alarcon do Passo; Subieta Vasquez, Martin Alfonso; Sugitate, Toru; Suire, Christophe Pierre; Sukhorukov, Mikhail; Sultanov, Rishat; Sumbera, Michal; Susa, Tatjana; Symons, Timothy; Szanto de Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szostak, Artur Krzysztof; Szymanski, Maciej; Takahashi, Jun; Tapia Takaki, Daniel Jesus; Tarazona Martinez, Alfonso; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terrevoli, Cristina; Thader, Jochen Mathias; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony; Toia, Alberica; Torii, Hisayuki; Tosello, Flavio; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ulery, Jason Glyndwr; Ullaland, Kjetil; Ulrich, Jochen; Uras, Antonio; Urban, Jozef; Urciuoli, Guido Marie; Usai, Gianluca; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; van Leeuwen, Marco; Vande Vyvre, Pierre; Vannucci, Luigi; Vargas, Aurora Diozcora; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vechernin, Vladimir; Veldhoen, Misha; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Vikhlyantsev, Oleg; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Viyogi, Yogendra; Vodopianov, Alexander; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; von Haller, Barthelemy; Vranic, Danilo; Øvrebekk, Gaute; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Boris; Wagner, Vladimir; Wan, Renzhuo; Wang, Dong; Wang, Mengliang; Wang, Yifei; Wang, Yaping; Watanabe, Kengo; Weber, Michael; Wessels, Johannes; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Alexander; Wilk, Grzegorz Andrzej; Williams, Crispin; Windelband, Bernd Stefan; Xaplanteris Karampatsos, Leonidas; Yaldo, Chris G; Yamaguchi, Yorito; Yang, Hongyan; Yang, Shiming; Yasnopolsky, Stanislav; Yi, JunGyu; Yin, Zhongbao; Yoo, In-Kwon; Yu, Weilin; Yuan, Xianbao; Yushmanov, Igor; Zaccolo, Valentina; Zach, Cenek; Zampolli, Chiara; Zaporozhets, Sergey; Zarochentsev, Andrey; Zavada, Petr; Zaviyalov, Nikolai; Zbroszczyk, Hanna Paulina; Zelnicek, Pierre; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhou, Daicui; Zhou, Fengchu; Zhou, You; Zhu, Jianhui; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zinovjev, Gennady; Zoccarato, Yannick Denis; Zynovyev, Mykhaylo; Zyzak, Maksym

2012-01-01

In this Letter we report the first results on $\\pi^\\pm$, K$^\\pm$, p and pbar production at mid-rapidity (|y|<0.5) in central Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV, measured by the ALICE experiment at the LHC. The pT distributions and yields are compared to previous results at $\\sqrt{s_{NN}}$ = 200 GeV and expectations from hydrodynamic and thermal models. The spectral shapes indicate a strong increase of the radial flow velocity with $\\sqrt{s_{NN}}$, which in hydrodynamic models is expected as a consequence of the increasing particle density. While the K/$\\pi$ ratio is in line with predictions from the thermal model, the p/$\\pi$ ratio is found to be lower by a factor of about 1.5. This deviation from thermal model expectations is still to be understood.

Hybrid rocket engine, theoretical model and experiment

Science.gov (United States)

Chelaru, Teodor-Viorel; Mingireanu, Florin

2011-06-01

The purpose of this paper is to build a theoretical model for the hybrid rocket engine/motor and to validate it using experimental results. The work approaches the main problems of the hybrid motor: the scalability, the stability/controllability of the operating parameters and the increasing of the solid fuel regression rate. At first, we focus on theoretical models for hybrid rocket motor and compare the results with already available experimental data from various research groups. A primary computation model is presented together with results from a numerical algorithm based on a computational model. We present theoretical predictions for several commercial hybrid rocket motors, having different scales and compare them with experimental measurements of those hybrid rocket motors. Next the paper focuses on tribrid rocket motor concept, which by supplementary liquid fuel injection can improve the thrust controllability. A complementary computation model is also presented to estimate regression rate increase of solid fuel doped with oxidizer. Finally, the stability of the hybrid rocket motor is investigated using Liapunov theory. Stability coefficients obtained are dependent on burning parameters while the stability and command matrixes are identified. The paper presents thoroughly the input data of the model, which ensures the reproducibility of the numerical results by independent researchers.

Hybrid simulation models of production networks

CERN Document Server

Kouikoglou, Vassilis S

2001-01-01

This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.

Analysis of chromosome aberration data by hybrid-scale models

International Nuclear Information System (INIS)

Indrawati, Iwiq; Kumazawa, Shigeru

2000-02-01

This paper presents a new methodology for analyzing data of chromosome aberrations, which is useful to understand the characteristics of dose-response relationships and to construct the calibration curves for the biological dosimetry. The hybrid scale of linear and logarithmic scales brings a particular plotting paper, where the normal section paper, two types of semi-log papers and the log-log paper are continuously connected. The hybrid-hybrid plotting paper may contain nine kinds of linear relationships, and these are conveniently called hybrid scale models. One can systematically select the best-fit model among the nine models by among the conditions for a straight line of data points. A biological interpretation is possible with some hybrid-scale models. In this report, the hybrid scale models were applied to separately reported data on chromosome aberrations in human lymphocytes as well as on chromosome breaks in Tradescantia. The results proved that the proposed models fit the data better than the linear-quadratic model, despite the demerit of the increased number of model parameters. We showed that the hybrid-hybrid model (both variables of dose and response using the hybrid scale) provides the best-fit straight lines to be used as the reliable and readable calibration curves of chromosome aberrations. (author)

Stress Assignment in N+N Combinations in Arabic

Directory of Open Access Journals (Sweden)

Abdel Rahman Mitib Altakhaineh

2017-09-01

Full Text Available The validity of stress as a criterion to distinguish between compounds and phrases has been investigated in many languages, including English (see e.g. Lieber 2005: 376; Booij 2012: 84. However, the possibility of using stress as a criterion in this way has not been investigated for Arabic. Siloni (1997: 21 claims that in N+N combinations in Semitic languages, stress always falls on the second element. However, the results of a study using PRAAT reveal that, in Modern Standard Arabic (MSA and Jordanian Arabic (JA, stress plays no role in distinguishing between various N+N combinations, i.e. compounds and phrases, e.g.ˈmuʕallim lfiizyaaʔ ‘the physics teacher’ vs.ˈbayt lwalad ‘the boy’s house’, respectively. Analysis shows that the default position of stress in N+N combinations in MSA and JA is on the first element. There is only one systematic exception, which is phonetically conditioned: in N+N combinations with assimilated geminates on the word boundary, a secondary stress or perhaps double stress is assigned.

Centrality determination using the Glauber model in Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV

CERN Document Server

2018-01-01

This document describes the methods used by the ALICE Collaboration for determining the centrality of Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV at the LHC within the Glauber model. The half-density radius $R$ and the diffusivity $a$ describing the Xe-129 nucleus with a 2-parameter Fermi distribution have been derived from a recent electron scattering measurement for the Xe-132 nucleus. The deformation parameter $\\beta$ has been derived by an interpolation between measured deformation parameters for the even-$A$ Xe isotopes. The inelastic nucleon-nucleon cross-section at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV has been estimated by interpolation of pp data at different center-of-mass energies. The particle multiplicity per nucleon-nucleon collision has been parameterized by a negative binomial distribution (NBD) and a number of independently emitting sources (ancestors) given by a linear combination of $N_{\\rm part}$ and $N_{\\rm coll}$. The NBD parameters $\\mu$ and $k$ and the ancestor parameter $f$ are then...

Hybrid Modelling of Individual Movement and Collective Behaviour

KAUST Repository

Franz, Benjamin

2013-01-01

Mathematical models of dispersal in biological systems are often written in terms of partial differential equations (PDEs) which describe the time evolution of population-level variables (concentrations, densities). A more detailed modelling approach is given by individual-based (agent-based) models which describe the behaviour of each organism. In recent years, an intermediate modelling methodology - hybrid modelling - has been applied to a number of biological systems. These hybrid models couple an individual-based description of cells/animals with a PDE-model of their environment. In this chapter, we overview hybrid models in the literature with the focus on the mathematical challenges of this modelling approach. The detailed analysis is presented using the example of chemotaxis, where cells move according to extracellular chemicals that can be altered by the cells themselves. In this case, individual-based models of cells are coupled with PDEs for extracellular chemical signals. Travelling waves in these hybrid models are investigated. In particular, we show that in contrary to the PDEs, hybrid chemotaxis models only develop a transient travelling wave. © 2013 Springer-Verlag Berlin Heidelberg.

Study of production and cold nuclear matter effects in pPb collisions at s NN √sNN = 5 TeV

NARCIS (Netherlands)

Aaij, R.; Adeva, B.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Cartelle, P. Alvarez; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; Everse, LA; Anderlini, L.; Anderson, J.; Andreassen, P.R.; Andreotti, M.; Andrews, J.E.; Appleby, R. B.; Gutierrez, O. Aquines; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J. J.; Badalov, A.; Balagura, V.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Belogurov, S.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bettler, M-O.; Van Beuzekom, Martin; Bien, A.; Bifani, S.; Bird, T.D.; Bizzeti, A.; Bjørnstad, P. M.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borghi, S.; Borgia, A.; Borsato, M.; Bowcock, T. J. V.; Bowen, E.; Bozzi, C.; Brambach, T.; Van Den Brand, J.; Bressieux, J.; Brett, D.; Britsch, M.; Britton, T.; Brodzicka, J.; Brook, N. H.; Brown, H.; Bursche, A.; Busetto, G.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Gomez, M. Calvo; Camboni, A.; Campana, P.; Perez, D. H. Campora; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carranza-Mejia, H.; Carson, L.; Akiba, K. Carvalho; Casse, G.; Cassina, L.; Garcia, L. Castillo; Cattaneo, M.; Cauet, Ch; Cenci, R.; Charles, M.; Charpentier, Ph; Chen, S.; Cheung, S-F.; Chiapolini, N.; Chrzaszcz, M.; Ciba, K.; Vidal, X. Cid; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Coquereau, S.; Corti, G.; Corvo, M.; Counts, I.; Couturier, B.; Cowan, G. A.; Craik, D. C.; Torres, M. Cruz; Cunliffe, S.; Currie, C.R.; D'Ambrosio, C.; Dalseno, J.; David, P.; Davis, A.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J. M.; Paula, L.E.; da-Silva, W.S.; De Simone, P.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Déléage, N.; Derkach, D.; Deschamps, O.; Dettori, F.; Di Canto, A.; Dijkstra, H.; Donleavy, S.; Dordei, F.; Dorigo, M.; Suárez, A. Dosil; Dossett, D.; Dovbnya, A.; Dujany, G.; Dupertuis, F.; 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, T. M.; Falabella, A.; Färber, C.; Farinelli, C.; Farley, N.; Farry, S.; Ferguson, D.; Albor, V. Fernandez; Rodrigues, F. Ferreira; Ferro-Luzzi, M.; Filippov, S.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fontana, Mark; Fontanelli, F.; Forty, R.; De Aguiar Francisco, O.; Frank, M.; Frei, C.; Frosini, M.; Fu, J.; Furfaro, E.; Torreira, A. Gallas; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; Garofoli, J.; Tico, J. Garra; Garrido, L.; Carvalho-Gaspar, M.; Gauld, Rhorry; Gavardi, L.; Gersabeck, E.; Gersabeck, M.; Gershon, T. J.; Ghez, Ph; Gianelle, A.; Giani, S.; Gibson, V.; Giubega, L.; Gligorov, V. V.; Göbel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.Q.; Head-Gordon, Teresa; Gotti, C.; Gándara, M. Grabalosa; Diaz, R. Graciani; Cardoso, L. A.Granado; Graugés, E.; Graziani, G.; Grecu, A.; Greening, E.; Gregson, S.; Griffith, P.; Grillo, L.; Grünberg, O.; Gui, B.; Gushchin, E.; Guz, Yu; Gys, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hall, S.; Hamilton, B.; Hampson, T.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; Hartmann, T.; He, J.; Head, T.; Heijne, V.; Hennessy, K.; Henrard, P.; Henry, L.; Morata, J. A.Hernando; van Herwijnen, E.; Heß, M.; Hicheur, A.; Hill, D.; Hoballah, M.; Hombach, C.; Hulsbergen, W.; Hunt, P.; Hussain, N.; Hutchcroft, D. E.; Hynds, D.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jaton, P.; Jawahery, A.; Jezabek, M.; Jing, F.; John, M.; Johnson, D.; Jones, C. R.; Joram, C.; Jost, B.; Jurik, N.; Kaballo, M.; Kandybei, S.; Kanso, W.; Karacson, M.; Karbach, T. M.; Kelsey, M. H.; Kenyon, I. R.; Ketel, T.; Khanji, B.; Khurewathanakul, C.; Klaver, S.M.; Kochebina, O.; Kolpin, M.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Korolev, M.; Kozlinskiy, A.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Kucharczyk, M.; Kudryavtsev, V.; Kurek, K.; Kvaratskheliya, T.; La Thi, V. N.; Lacarrere, D.; Lafferty, G. D.; Lai, A.; Lambert, D.M.; Lambert, R. W.; Lanciotti, E.; Lanfranchi, G.; Langenbruch, C.; Langhans, B.; Latham, T. E.; Lazzeroni, C.; Le Gac, R.; Van Leerdam, J.; Lees, J. P.; Lefèvre, R.; Leflat, A.; Lefrançois, J.; Di Leo, S.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Liles, M.; Lindner, R.; Linn, S.C.; Lionetto, F.; Liu, B.; Liu, G.; Lohn, S.; Longstaff, I.; Lopes, J. H.; Lopez-March, N.; Lowdon, P.; Lu, H.; Lucchesi, D.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Machefert, F.; Machikhiliyan, I. V.; Maciuc, F.; Maev, O.; Malde, S.; Manca, G.; Mancinelli, G.; Manzali, M.; Maratas, J.; Marchand, J. F.; Marconi, U.; Benito, C. Marin; Marino, P.; Märki, R.; Marks, J.; Martellotti, G.; Martens, A.; Sánchez, A. Martín; Martinelli-Boneschi, F.; Santos, D. Martinez; Vidal, F. Martinez; Tostes, D. Martins; Massafferri, A.; Matev, R.; Mathe, Z.; Matteuzzi, C.; Mazurov, A.; McCann, M.; McCarthy, J.; Mcnab, A.; McNulty, R.; McSkelly, B.; Meadows, B. T.; Meier, F.; Meissner, M.; Merk, M.; Milanes, D. A.; Minard, M. N.; Moggi, N.; Rodriguez, J. Molina; Monteil, S.; Moran-Zenteno, D.; Morandin, M.; Morawski, P.; Mordà, A.; Morello, M. J.; Moron, J.; Morris, A. B.; Mountain, R.; Muheim, F.; Müller, Karl; Muresan, R.; Mussini, M.; Muster, B.; Naik, P.; Nakada, T.; Nandakumar, R.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A. D.; Nguyen, T. D.; Nguyen-Mau, C.; Nicol, M.; Niess, V.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; Oblakowska-Mucha, A.; Obraztsov, V.; Oggero, S.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Onderwater, G.; Orlandea, M.; Goicochea, J. M.Otalora; Owen, R.P.; Oyanguren, A.; Pal, B. K.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Parkes, C.; Parkinson, C. J.; Passaleva, G.; Patel, G. D.; Patel, M.; Patrignani, C.; Alvarez, A. Pazos; Pearce, D.A.; Pellegrino, A.; Altarelli, M. Pepe; Perazzini, S.; Trigo, E. Perez; Perret, P.; Perrin-Terrin, M.; Pescatore, L.; Pesen, E.; Petridis, K.; Petrolini, A.; Olloqui, E. Picatoste; Pietrzyk, B.; Pilař, T.; Pinci, D.; Pistone, A.; Playfer, S.; Casasus, M. Plo; Polci, F.; Poluektov, A.; Polycarpo, E.; Popov, A.; Popov, D.; Popovici, B.; Potterat, C.; Powell, A.; Prisciandaro, J.; Pritchard, C.A.; Prouve, C.; Pugatch, V.; Navarro, A. Puig; Punzi, G.; Qian, Y.W.; Rachwal, B.; Rademacker, J. H.; Rakotomiaramanana, B.; Rama, M.; Rangel, M. S.; Raniuk, I.; Rauschmayr, N.; Raven, G.; Reichert, S.; Reid, M.; dos Reis, A. C.; Ricciardi, S.; Richards, Al.; Rihl, M.; Rinnert, K.; Molina, V. Rives; Romero, D. A.Roa; Robbe, P.; Rodrigues, A. B.; Rodrigues, L.E.T.; Perez, P. Rodriguez; Roiser, S.; Romanovsky, V.; Vidal, A. Romero; Rotondo, M.; Rouvinet, J.; Ruf, T.; Ruffini, F.; Ruiz, van Hapere; Valls, P. Ruiz; Sabatino, G.; Silva, J. J.Saborido; Sagidova, N.; Sail, P.; Saitta, B.; Guimaraes, V. Salustino; Mayordomo, C. Sanchez; Sedes, B. Sanmartin; Santacesaria, R.; Rios, C. Santamarina; Santovetti, E.; Sapunov, M.; Sarti, A.; Satriano, C.; Satta, A.; Savrie, M.; Savrina, D.; Schiller, M.; Schindler, R. H.; Schlupp, M.; Schmelling, M.; Schmidt, B.; Schneider, O.; Schopper, A.; Schune, M. H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Seco, M.; Semennikov, A.; Senderowska, K.; Sepp, I.; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Coutinho, R. Silva; Simi, G.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, N. A.; Smith, E.; Smith, E.; Smith, J; Smith, M.; Snoek, H.; Sokoloff, M. D.; Soler, F. J. P.; Soomro, F.; de Souza, D.K.; De Paula, B. Souza; Spaan, B.; Sparkes, A.; Spinella, F.; Spradlin, P.; Stagni, F.; Stahl, S.; Steinkamp, O.; Stenyakin, O.; Stevenson-Moore, P.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Stroili, R.; Subbiah, V. K.; Sun, L.; Sutcliffe, W.; Swientek, K.; Swientek, S.; Syropoulos, V.; Szczekowski, M.; Szczypka, P.; Szilard, D.; Szumlak, T.; T'Jampens, S.; Teklishyn, M.; Tellarini, G.; Teubert, F.; Thomas, C.; Thomas, E.; Van Tilburg, J.; Tisserand, V.; Tobin, M. N.; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Torr, N.; Tournefier, E.; Tourneur, S.; Tran, N.T.M.T.; Tresch, M.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Garcia, M. Ubeda; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vagnoni, V.; Valenti, G.; Vallier, A.; Gomez, R. Vazquez; Regueiro, P. Vazquez; Sierra, C. Vázquez; Vecchi, S.; Velthuis, M.J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Vieira, D.; Diaz, M. Vieites; Vilasis-Cardona, X.; Vollhardt, A.; Volyanskyy, D.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voß, C.; Voss, H.; De Vries, J. A.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, John; Wandernoth, S.; Wang, J.; Ward, D. R.; Watson, N. K.; Websdale, D.; Whitehead, M.; Wicht, J.; Wiedner, D.; Wilkinson, G.; Williams, M.P.; Williams, M.; Wilson, James F; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S. A.; Wright, S.J.; Wu, S.; Wyllie, K.; Xie, Y.; Xing, Z.; Xu, Z.; Yang, Z.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, F.; Zhang, L.; Zhang, W. C.; Zhang, Y.; Zhelezov, A.; Zhokhov, A.; Zhong, L.; Zvyagin, A.

2014-01-01

Production of mesons in proton-lead collisions at a nucleon-nucleon centre-of-mass energy s NN √sNN = 5 TeV is studied with the LHCb detector. The analysis is based on a data sample corresponding to an integrated luminosity of 1.6 nb-1. The mesons of transverse momenta up to 15 GeV/c are

Effective field theory for NN interactions

International Nuclear Information System (INIS)

Tran Duy Khuong; Vo Hanh Phuc

2003-01-01

The effective field theory of NN interactions is formulated and the power counting appropriate to this case is reviewed. It is more subtle than in most effective field theories since in the limit that the S-wave NN scattering lengths go to infinity. It is governed by nontrivial fixed point. The leading two body terms in the effective field theory for nucleon self interactions are scale invariant and invariant under Wigner SU(4) spin-isospin symmetry in this limit. Higher body terms with no derivatives (i.e. three and four body terms) are automatically invariant under Wigner symmetry. (author)

DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive

International Nuclear Information System (INIS)

Akkaya, R.; Kulaksiz, A.A.; Aydogdu, O.

2007-01-01

This paper presents a brushless dc motor drive for heating, ventilating and air conditioning fans, which is utilized as the load of a photovoltaic system with a maximum power point tracking (MPPT) controller. The MPPT controller is based on a genetic assisted, multi-layer perceptron neural network (GA-MLP-NN) structure and includes a DC-DC boost converter. Genetic assistance in the neural network is used to optimize the size of the hidden layer. Also, for training the network, a genetic assisted, Levenberg-Marquardt (GA-LM) algorithm is utilized. The off line GA-MLP-NN, trained by this hybrid algorithm, is utilized for online estimation of the voltage and current values in the maximum power point. A brushless dc (BLDC) motor drive system that incorporates a motor controller with proportional integral (PI) speed control loop is successfully implemented to operate the fans. The digital signal processor (DSP) based unit provides rapid achievement of the MPPT and current control of the BLDC motor drive. The performance results of the system are given, and experimental results are presented for a laboratory prototype of 120 W

More about the comparison of local and non-local NN interaction models

International Nuclear Information System (INIS)

Amghar, A.; Desplanques, B.

2003-01-01

The effect of non-locality in the NN interaction with an off-energy shell character has been studied in the past in relation with the possibility that some models could be approximately phase-shifts equivalent. This work is extended to a non-locality implying terms that involve an anticommutator with the operator p 2 . It includes both scalar and tensor components. The most recent 'high accuracy' models are considered in the analysis. After studying the deuteron wave functions, electromagnetic properties of various models are compared with the idea that these ones differ by their non-locality but are equivalent up to a unitary transformation. It is found that the extra non-local tensor interaction considered in this work tends to re-enforce the role of the term considered in previous works, allowing one to explain almost completely the difference in the deuteron D-state probabilities evidenced by the comparison of the Bonn-QB and Paris models for instance. Conclusions for the effect of the non-local scalar interaction are not so clear. In many cases, it was found that these terms could explain part of the differences that the comparison of predictions for various models evidences but cases where they could not were also found. Some of these last ones have been analyzed in order to pointing out the origin of the failure

Exploratory Topology Modelling of Form-Active Hybrid Structures

DEFF Research Database (Denmark)

Holden Deleuran, Anders; Pauly, Mark; Tamke, Martin

2016-01-01

The development of novel form-active hybrid structures (FAHS) is impeded by a lack of modelling tools that allow for exploratory topology modelling of shaped assemblies. We present a flexible and real-time computational design modelling pipeline developed for the exploratory modelling of FAHS...... that enables designers and engineers to iteratively construct and manipulate form-active hybrid assembly topology on the fly. The pipeline implements Kangaroo2's projection-based methods for modelling hybrid structures consisting of slender beams and cable networks. A selection of design modelling sketches...

GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.

Directory of Open Access Journals (Sweden)

Ahmed Shamsul Arefin

Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.

A kNN method that uses a non-natural evolutionary algorithm for ...

African Journals Online (AJOL)

We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...

Hybrid discrete choice models: Gained insights versus increasing effort

Energy Technology Data Exchange (ETDEWEB)

Mariel, Petr, E-mail: petr.mariel@ehu.es [UPV/EHU, Economía Aplicada III, Avda. Lehendakari Aguire, 83, 48015 Bilbao (Spain); Meyerhoff, Jürgen [Institute for Landscape Architecture and Environmental Planning, Technical University of Berlin, D-10623 Berlin, Germany and The Kiel Institute for the World Economy, Duesternbrooker Weg 120, 24105 Kiel (Germany)

2016-10-15

Hybrid choice models expand the standard models in discrete choice modelling by incorporating psychological factors as latent variables. They could therefore provide further insights into choice processes and underlying taste heterogeneity but the costs of estimating these models often significantly increase. This paper aims at comparing the results from a hybrid choice model and a classical random parameter logit. Point of departure for this analysis is whether researchers and practitioners should add hybrid choice models to their suite of models routinely estimated. Our comparison reveals, in line with the few prior studies, that hybrid models gain in efficiency by the inclusion of additional information. The use of one of the two proposed approaches, however, depends on the objective of the analysis. If disentangling preference heterogeneity is most important, hybrid model seems to be preferable. If the focus is on predictive power, a standard random parameter logit model might be the better choice. Finally, we give recommendations for an adequate use of hybrid choice models based on known principles of elementary scientific inference. - Highlights: • The paper compares performance of a Hybrid Choice Model (HCM) and a classical Random Parameter Logit (RPL) model. • The HCM indeed provides insights regarding preference heterogeneity not gained from the RPL. • The RPL has similar predictive power as the HCM in our data. • The costs of estimating HCM seem to be justified when learning more on taste heterogeneity is a major study objective.

Hybrid discrete choice models: Gained insights versus increasing effort

International Nuclear Information System (INIS)

Mariel, Petr; Meyerhoff, Jürgen

2016-01-01

Hybrid choice models expand the standard models in discrete choice modelling by incorporating psychological factors as latent variables. They could therefore provide further insights into choice processes and underlying taste heterogeneity but the costs of estimating these models often significantly increase. This paper aims at comparing the results from a hybrid choice model and a classical random parameter logit. Point of departure for this analysis is whether researchers and practitioners should add hybrid choice models to their suite of models routinely estimated. Our comparison reveals, in line with the few prior studies, that hybrid models gain in efficiency by the inclusion of additional information. The use of one of the two proposed approaches, however, depends on the objective of the analysis. If disentangling preference heterogeneity is most important, hybrid model seems to be preferable. If the focus is on predictive power, a standard random parameter logit model might be the better choice. Finally, we give recommendations for an adequate use of hybrid choice models based on known principles of elementary scientific inference. - Highlights: • The paper compares performance of a Hybrid Choice Model (HCM) and a classical Random Parameter Logit (RPL) model. • The HCM indeed provides insights regarding preference heterogeneity not gained from the RPL. • The RPL has similar predictive power as the HCM in our data. • The costs of estimating HCM seem to be justified when learning more on taste heterogeneity is a major study objective.

Prediction and Optimization of Key Performance Indicators in the Production of Stator Core Using a GA-NN Approach

Science.gov (United States)

Rajora, M.; Zou, P.; Xu, W.; Jin, L.; Chen, W.; Liang, S. Y.

2017-12-01

With the rapidly changing demands of the manufacturing market, intelligent techniques are being used to solve engineering problems due to their ability to handle nonlinear complex problems. For example, in the conventional production of stator cores, it is relied upon experienced engineers to make an initial plan on the number of compensation sheets to be added to achieve uniform pressure distribution throughout the laminations. Additionally, these engineers must use their experience to revise the initial plans based upon the measurements made during the production of stator core. However, this method yields inconsistent results as humans are incapable of storing and analysing large amounts of data. In this article, first, a Neural Network (NN), trained using a hybrid Levenberg-Marquardt (LM) - Genetic Algorithm (GA), is developed to assist the engineers with the decision-making process. Next, the trained NN is used as a fitness function in an optimization algorithm to find the optimal values of the initial compensation sheet plan with the aim of minimizing the required revisions during the production of the stator core.

Hybrid Models of Alternative Current Filter for Hvdc

Directory of Open Access Journals (Sweden)

Ufa Ruslan A.

2017-01-01

Full Text Available Based on a hybrid simulation concept of HVDC, the developed hybrid AC filter models, providing the sufficiently full and adequate modeling of all single continuous spectrum of quasi-steady-state and transient processes in the filter, are presented. The obtained results suggest that usage of the hybrid simulation approach is carried out a methodically accurate with guaranteed instrumental error solution of differential equation systems of mathematical models of HVDC.

Some hybrid models applicable to dose-response relationships

International Nuclear Information System (INIS)

Kumazawa, Shigeru

1992-01-01

A new type of models of dose-response relationships has been studied as an initial stage to explore a reliable extrapolation of the relationships decided by high dose data to the range of low dose covered by radiation protection. The approach is to use a 'hybrid scale' of linear and logarithmic scales; the first model is that the normalized surviving fraction (ρ S > 0) in a hybrid scale decreases linearly with dose in a linear scale, and the second is that the induction in a log scale increases linearly with the normalized dose (τ D > 0) in a hybrid scale. The hybrid scale may reflect an overall effectiveness of a complex system against adverse events caused by various agents. Some data of leukemia in the atomic bomb survivors and of rodent experiments were used to show the applicability of hybrid scale models. The results proved that proposed models fit these data not less than the popular linear-quadratic models, providing the possible interpretation of shapes of dose-response curves, e.g. shouldered survival curves varied by recovery time. (author)

Evidence for a narrow NN-bar state at 2.02 GEV/C2 in 6 and 9 GEV/C antiproton interactions

International Nuclear Information System (INIS)

Azooz, F.; Butterworth, I.; Dornan, P.J.

1982-11-01

Evidence for the existence of a charged narrow state of mass M approximately 2.02 GeV/c 2 and width GAMMA approximately 2 , decaying into NN-bar is reported. The state was observed in the reaction p-barp → psub(fast)n-barπ + π - π - at 6 GeV/c and in p-barp → π + sub(fast)p-barnπ + π - at 9 GeV/c in a triggered bubble chamber experiment at the SLAC Hybrid Facility. (author)

Modelling the solar wind interaction with Mercury by a quasi-neutral hybrid model

Directory of Open Access Journals (Sweden)

E. Kallio

2003-11-01

Full Text Available Quasi-neutral hybrid model is a self-consistent modelling approach that includes positively charged particles and an electron fluid. The approach has received an increasing interest in space plasma physics research because it makes it possible to study several plasma physical processes that are difficult or impossible to model by self-consistent fluid models, such as the effects associated with the ions’ finite gyroradius, the velocity difference between different ion species, or the non-Maxwellian velocity distribution function. By now quasi-neutral hybrid models have been used to study the solar wind interaction with the non-magnetised Solar System bodies of Mars, Venus, Titan and comets. Localized, two-dimensional hybrid model runs have also been made to study terrestrial dayside magnetosheath. However, the Hermean plasma environment has not yet been analysed by a global quasi-neutral hybrid model. In this paper we present a new quasi-neutral hybrid model developed to study various processes associated with the Mercury-solar wind interaction. Emphasis is placed on addressing advantages and disadvantages of the approach to study different plasma physical processes near the planet. The basic assumptions of the approach and the algorithms used in the new model are thoroughly presented. Finally, some of the first three-dimensional hybrid model runs made for Mercury are presented. The resulting macroscopic plasma parameters and the morphology of the magnetic field demonstrate the applicability of the new approach to study the Mercury-solar wind interaction globally. In addition, the real advantage of the kinetic hybrid model approach is to study the property of individual ions, and the study clearly demonstrates the large potential of the approach to address these more detailed issues by a quasi-neutral hybrid model in the future.Key words. Magnetospheric physics (planetary magnetospheres; solar wind-magnetosphere interactions – Space plasma

Hybrid computer modelling in plasma physics

International Nuclear Information System (INIS)

Hromadka, J; Ibehej, T; Hrach, R

2016-01-01

Our contribution is devoted to development of hybrid modelling techniques. We investigate sheath structures in the vicinity of solids immersed in low temperature argon plasma of different pressures by means of particle and fluid computer models. We discuss the differences in results obtained by these methods and try to propose a way to improve the results of fluid models in the low pressure area. There is a possibility to employ Chapman-Enskog method to find appropriate closure relations of fluid equations in a case when particle distribution function is not Maxwellian. We try to follow this way to enhance fluid model and to use it in hybrid plasma model further. (paper)

Classification in medical image analysis using adaptive metric k-NN

DEFF Research Database (Denmark)

Chen, Chen; Chernoff, Konstantin; Karemore, Gopal

2010-01-01

The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier...

Modeling of Hybrid Growth Wastewater Bio-reactor

International Nuclear Information System (INIS)

EI Nashaei, S.; Garhyan, P.; Prasad, P.; Abdel Halim, H.S.; Ibrahim, G.

2004-01-01

The attached/suspended growth mixed reactors are considered one of the recently tried approaches to improve the performance of the biological treatment by increasing the volume of the accumulated biomass in terms of attached growth as well as suspended growth. Moreover, the domestic WW can be easily mixed with a high strength non-hazardous industrial wastewater and treated together in these bio-reactors if the need arises. Modeling of Hybrid hybrid growth wastewater reactor addresses the need of understanding the rational of such system in order to achieve better design and operation parameters. This paper aims at developing a heterogeneous mathematical model for hybrid growth system considering the effect of diffusion, external mass transfer, and power input to the system in a rational manner. The model will be based on distinguishing between liquid/solid phase (bio-film and bio-floc). This model would be a step ahead to the fine tuning the design of hybrid systems based on the experimental data of a pilot plant to be implemented in near future

Modelling the solar wind interaction with Mercury by a quasi-neutral hybrid model

Directory of Open Access Journals (Sweden)

E. Kallio

Full Text Available Quasi-neutral hybrid model is a self-consistent modelling approach that includes positively charged particles and an electron fluid. The approach has received an increasing interest in space plasma physics research because it makes it possible to study several plasma physical processes that are difficult or impossible to model by self-consistent fluid models, such as the effects associated with the ions’ finite gyroradius, the velocity difference between different ion species, or the non-Maxwellian velocity distribution function. By now quasi-neutral hybrid models have been used to study the solar wind interaction with the non-magnetised Solar System bodies of Mars, Venus, Titan and comets. Localized, two-dimensional hybrid model runs have also been made to study terrestrial dayside magnetosheath. However, the Hermean plasma environment has not yet been analysed by a global quasi-neutral hybrid model.

In this paper we present a new quasi-neutral hybrid model developed to study various processes associated with the Mercury-solar wind interaction. Emphasis is placed on addressing advantages and disadvantages of the approach to study different plasma physical processes near the planet. The basic assumptions of the approach and the algorithms used in the new model are thoroughly presented. Finally, some of the first three-dimensional hybrid model runs made for Mercury are presented.

The resulting macroscopic plasma parameters and the morphology of the magnetic field demonstrate the applicability of the new approach to study the Mercury-solar wind interaction globally. In addition, the real advantage of the kinetic hybrid model approach is to study the property of individual ions, and the study clearly demonstrates the large potential of the approach to address these more detailed issues by a quasi-neutral hybrid model in the future.

Key words. Magnetospheric physics

NN resonance and the corrections to Goldberger-Treiman relation

International Nuclear Information System (INIS)

Bhamathi, G.; Raghavan, S.

1977-01-01

The relevance of the recent experimental observation of possible bound and resonant states in NN scattering to the Goldberger-Treiman (GT) relation is examined. It is pointed out that an S-wave resonance in NN scattering goes a long way towards accounting for the corrections to the GT relations. Values of the mass and width of the resonance capable of giving a reasonable fit for the GT relation are presented. (author)

A hybrid absorbing boundary condition for frequency-domain finite-difference modelling

International Nuclear Information System (INIS)

Ren, Zhiming; Liu, Yang

2013-01-01

Liu and Sen (2010 Geophysics 75 A1–6; 2012 Geophys. Prospect. 60 1114–32) proposed an efficient hybrid scheme to significantly absorb boundary reflections for acoustic and elastic wave modelling in the time domain. In this paper, we extend the hybrid absorbing boundary condition (ABC) into the frequency domain and develop specific strategies for regular-grid and staggered-grid modelling, respectively. Numerical modelling tests of acoustic, visco-acoustic, elastic and vertically transversely isotropic (VTI) equations show significant absorptions for frequency-domain modelling. The modelling results of the Marmousi model and the salt model also demonstrate the effectiveness of the hybrid ABC. For elastic modelling, the hybrid Higdon ABC and the hybrid Clayton and Engquist (CE) ABC are implemented, respectively. Numerical simulations show that the hybrid Higdon ABC gets better absorption than the hybrid CE ABC, especially for S-waves. We further compare the hybrid ABC with the classical perfectly matched layer (PML). Results show that the two ABCs cost the same computation time and memory space for the same absorption width. However, the hybrid ABC is more effective than the PML for the same small absorption width and the absorption effects of the two ABCs gradually become similar when the absorption width is increased. (paper)

Model Reduction of Hybrid Systems

DEFF Research Database (Denmark)

Shaker, Hamid Reza

gramians. Generalized gramians are the solutions to the observability and controllability Lyapunov inequalities. In the first framework the projection matrices are found based on the common generalized gramians. This framework preserves the stability of the original switched system for all switching...... is guaranteed to be preserved for arbitrary switching signal. To compute the common generalized gramians linear matrix inequalities (LMI’s) need to be solved. These LMI’s are not always feasible. In order to solve the problem of conservatism, the second framework is presented. In this method the projection......High-Technological solutions of today are characterized by complex dynamical models. A lot of these models have inherent hybrid/switching structure. Hybrid/switched systems are powerful models for distributed embedded systems design where discrete controls are applied to continuous processes...

Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection

Science.gov (United States)

Safi’ie, M. A.; Utami, E.; Fatta, H. A.

2018-03-01

Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.

Mathematical Modeling of Hybrid Electrical Engineering Systems

Directory of Open Access Journals (Sweden)

A. A. Lobaty

2016-01-01

Full Text Available A large class of systems that have found application in various industries and households, electrified transportation facilities and energy sector has been classified as electrical engineering systems. Their characteristic feature is a combination of continuous and discontinuous modes of operation, which is reflected in the appearance of a relatively new term “hybrid systems”. A wide class of hybrid systems is pulsed DC converters operating in a pulse width modulation, which are non-linear systems with variable structure. Using various methods for linearization it is possible to obtain linear mathematical models that rather accurately simulate behavior of such systems. However, the presence in the mathematical models of exponential nonlinearities creates considerable difficulties in the implementation of digital hardware. The solution can be found while using an approximation of exponential functions by polynomials of the first order, that, however, violates the rigor accordance of the analytical model with characteristics of a real object. There are two practical approaches to synthesize algorithms for control of hybrid systems. The first approach is based on the representation of the whole system by a discrete model which is described by difference equations that makes it possible to synthesize discrete algorithms. The second approach is based on description of the system by differential equations. The equations describe synthesis of continuous algorithms and their further implementation in a digital computer included in the control loop system. The paper considers modeling of a hybrid electrical engineering system using differential equations. Neglecting the pulse duration, it has been proposed to describe behavior of vector components in phase coordinates of the hybrid system by stochastic differential equations containing generally non-linear differentiable random functions. A stochastic vector-matrix equation describing dynamics of the

Probabilistic modelling and analysis of stand-alone hybrid power systems

International Nuclear Information System (INIS)

Lujano-Rojas, Juan M.; Dufo-López, Rodolfo; Bernal-Agustín, José L.

2013-01-01

As a part of the Hybrid Intelligent Algorithm, a model based on an ANN (artificial neural network) has been proposed in this paper to represent hybrid system behaviour considering the uncertainty related to wind speed and solar radiation, battery bank lifetime, and fuel prices. The Hybrid Intelligent Algorithm suggests a combination of probabilistic analysis based on a Monte Carlo simulation approach and artificial neural network training embedded in a genetic algorithm optimisation model. The installation of a typical hybrid system was analysed. Probabilistic analysis was used to generate an input–output dataset of 519 samples that was later used to train the ANNs to reduce the computational effort required. The generalisation ability of the ANNs was measured in terms of RMSE (Root Mean Square Error), MBE (Mean Bias Error), MAE (Mean Absolute Error), and R-squared estimators using another data group of 200 samples. The results obtained from the estimation of the expected energy not supplied, the probability of a determined reliability level, and the estimation of expected value of net present cost show that the presented model is able to represent the main characteristics of a typical hybrid power system under uncertain operating conditions. - Highlights: • This paper presents a probabilistic model for stand-alone hybrid power system. • The model considers the main sources of uncertainty related to renewable resources. • The Hybrid Intelligent Algorithm has been applied to represent hybrid system behaviour. • The installation of a typical hybrid system was analysed. • The results obtained from the study case validate the presented model

Search for weakly decaying Λn‾ and ΛΛ exotic bound states in central Pb–Pb collisions at sNN=2.76 TeV

Directory of Open Access Journals (Sweden)

J. Adam

2016-01-01

Full Text Available We present results of a search for two hypothetical strange dibaryon states, i.e. the H-dibaryon and the possible Λn‾ bound state. The search is performed with the ALICE detector in central (0–10% Pb–Pb collisions at sNN=2.76 TeV, by invariant mass analysis in the decay modes Λn‾→d‾π+ and H-dibaryon →Λpπ−. No evidence for these bound states is observed. Upper limits are determined at 99% confidence level for a wide range of lifetimes and for the full range of branching ratios. The results are compared to thermal, coalescence and hybrid UrQMD model expectations, which describe correctly the production of other loosely bound states, like the deuteron and the hypertriton.

A 10 nN resolution thrust-stand for micro-propulsion devices

Energy Technology Data Exchange (ETDEWEB)

Chakraborty, Subha; Courtney, Daniel G.; Shea, Herbert, E-mail: herbert.shea@epfl.ch [Microsystems for Space Technologies Laboratory (LMTS), Ecole Polytechnique Federale de Lausanne (EPFL), Neuchatel (Switzerland)

2015-11-15

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.

Modelling dependable systems using hybrid Bayesian networks

International Nuclear Information System (INIS)

Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter

2008-01-01

A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems

Sum rules for the ed - NN scattering reactions and microscopic potential field-theoretical approach

International Nuclear Information System (INIS)

Machivariani, A.I.

1996-01-01

The connections between the equal-time commutators of nucleon and photon field-operators and relativistic potential approach of ed - NN scattering equations is established. Namely, it is demonstrated that: 1) equal-time commutator between nucleon field operators generated completeness condition for NN interaction functions, 2) the off-mass shell contributions in γd - NN exchange currents or in microscopic NN potential are determined by equal time commutator between nucleon field operator and photon or nucleon source operators, and 3) equal-time commutators between source operators produce sum rules for same vertex functions and effective potentials [ru

Hybrid photovoltaic–thermal solar collectors dynamic modeling

International Nuclear Information System (INIS)

Amrizal, N.; Chemisana, D.; Rosell, J.I.

2013-01-01

Highlights: ► A hybrid photovoltaic/thermal dynamic model is presented. ► The model, once calibrated, can predict the power output for any set of climate data. ► The physical electrical model includes explicitly thermal and irradiance dependences. ► The results agree with those obtained through steady-state characterization. ► The model approaches the junction cell temperature through the system energy balance. -- Abstract: A hybrid photovoltaic/thermal transient model has been developed and validated experimentally. The methodology extends the quasi-dynamic thermal model stated in the EN 12975 in order to involve the electrical performance and consider the dynamic behavior minimizing constraints when characterizing the collector. A backward moving average filtering procedure has been applied to improve the model response for variable working conditions. Concerning the electrical part, the model includes the thermal and radiation dependences in its variables. The results revealed that the characteristic parameters included in the model agree reasonably well with the experimental values obtained from the standard steady-state and IV characteristic curve measurements. After a calibration process, the model is a suitable tool to predict the thermal and electrical performance of a hybrid solar collector, for a specific weather data set.

Evaporator modeling - A hybrid approach

International Nuclear Information System (INIS)

Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun

2009-01-01

In this paper, a hybrid modeling approach is proposed to model two-phase flow evaporators. The main procedures for hybrid modeling includes: (1) Based on the energy and material balance, and thermodynamic principles to formulate the process fundamental governing equations; (2) Select input/output (I/O) variables responsible to the system performance which can be measured and controlled; (3) Represent those variables existing in the original equations but are not measurable as simple functions of selected I/Os or constants; (4) Obtaining a single equation which can correlate system inputs and outputs; and (5) Identify unknown parameters by linear or nonlinear least-squares methods. The method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating evaporator with the maximum error of ±8%. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis

Influence of six-quark bags on the NN interaction in a resonating group scattering calculation

International Nuclear Information System (INIS)

Zhang Zongye; Braeuer, K.; Faessler, A.; Shimizu, K.

1985-01-01

The influence of six-quark bags oin the nucleon-nucleon (NN) interaction is studied in a dynamical calculation of the NN scattering process. The NN interaction is described by the exchange of gluons and pions between quarks and a phenomenological sigma-meson exchange between nucleons. The quark wave functions are harmonic oscillators and the relative wave function between the two nucleons is determined by the resonating group method. At short distances the NN system is allowed to fuse to a six-quark bag where all six quarks are in a ground state or where two quarks are in excited Op states. The sizes of these six-quark bags are dynamical parameters in the resonating group calculation allowing for spatial polarisation effects during the interaction. The S-wave NN scattering data can be reproduced by adjusting the sigma-coupling strength. The main result is that the six-quark bags with an increased radius have a large influence on the NN scattering process. (orig.)

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Science.gov (United States)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2015-01-01

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

A Novel Hybrid Similarity Calculation Model

Directory of Open Access Journals (Sweden)

Xiaoping Fan

2017-01-01

Full Text Available This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.

Application of CA and NN for event recognition in experiments DISTO and STREAMER

International Nuclear Information System (INIS)

Bussa, M.P.; Ivanov, V.V.; Kisel, I.V.; Pontecorvo, G.B.

1997-01-01

An algorithm for charged particle recognition and event identification applying a cellular automaton (CA) model and a multi-layer neural network (NN) has been developed for the DISTO experiment under way at Saturne (Saclay, France). A further development of the model will be applied for particle recognition in the Dubna Streamer Chamber Spectrometer (DSCS) for studying pion-nucleus absorption (experiments DISTO and STREAMER). (orig.)

Hybrid attacks on model-based social recommender systems

Science.gov (United States)

Yu, Junliang; Gao, Min; Rong, Wenge; Li, Wentao; Xiong, Qingyu; Wen, Junhao

2017-10-01

With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

Bond graph model-based fault diagnosis of hybrid systems

CERN Document Server

Borutzky, Wolfgang

2015-01-01

This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

Heat control in HVDC resistive divider by PID and NN controllers

International Nuclear Information System (INIS)

Yilmaz, S.; Dincer, H.; Eksin, I.; Kalenderli, O.

2007-01-01

In this study, a control system is presented that is devised to increase measurement precisions within a prototype high voltage DC resistive divider (HVDC-RD). Since one of the major sources of measurement errors in such devices is the self heating effect, a system controlling the temperature within the high voltage DC resistive divider is devised so that suitable and stable temperature conditions are maintained that, in return, will decrease the measurement errors. The resistive divider system is cooled by oil, and PID and neural network (NN) controllers try to keep the temperature within the prescribed limits. The system to be controlled exhibits a nonlinear character, and therefore, a control approach based on NN controllers is proposed. Thus, a system that can fulfill the various requirements dictated by the designer is constructed. The performance of the NN controller is compared with that of the PID controller developed for the same purpose, and the values of the performance indices indicate the superiority of the NN controller over that of the classical PID controller

Modelling and control of a light-duty hybrid electric truck

OpenAIRE

Park, Jong-Kyu

2006-01-01

This study is concentrated on modelling and developing the controller for the light-duty hybrid electric truck. The hybrid electric vehicle has advantages in fuel economy. However, there have been relatively few studies on commercial HEVs, whilst a considerable number of studies on the hybrid electric system have been conducted in the field of passenger cars. So the current status and the methodologies to develop the LD hybrid electric truck model have been studied through the ...

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Hybrid magnet devices for molecule manipulation and small scale high gradient-field applications

Science.gov (United States)

Humphries, David E [El Cerrito, CA; Hong, Seok-Cheol [Seoul, KR; Cozzarelli, legal representative, Linda A.; Pollard, Martin J [El Cerrito, CA; Cozzarelli, Nicholas R [Berkeley, CA

2009-01-06

The present disclosure provides a high performance hybrid magnetic structure made from a combination of permanent magnets and ferromagnetic pole materials which are assembled in a predetermined array. The hybrid magnetic structure provides means for separation and other biotechnology applications involving holding, manipulation, or separation of magnetizable molecular structures and targets. Also disclosed are hybrid magnetic tweezers able to exert approximately 1 nN of force to 4.5 .mu.m magnetic bead. The maximum force was experimentally measured to be .about.900 pN which is in good agreement with theoretical estimations and other measurements. In addition, a new analysis scheme that permits fast real-time position measurement in typical geometry of magnetic tweezers has been developed and described in detail.

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

Science.gov (United States)

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

2017-05-01

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

Photon production in Pb + Pb collisions at √{sNN } = 2.76 TeV

Science.gov (United States)

Fu, Yong-Ping; Xi, Qin

2018-02-01

We calculate the high energy photon production from the Pb + Pb collisions for different centrality classes at √{sNN } = 2.76 TeV Large Hadron Collider (LHC) energy. The jet energy loss in the jet fragmentation, jet-photon conversion and jet bremsstrahlung is considered by using the Wang-Huang-Sarcevic (WHS) and Baier-Dokshitzer-Mueller-Peigne-Schiff (BDMPS) models. We use the (1 + 1)-dimensional ideal relativistic hydrodynamics to study the collective transverse flow and space-time evolution of the quark gluon plasma (QGP). The numerical results agree well with the ALICE data of the direct photons from the Pb + Pb collisions (√{sNN } = 2.76 TeV) for 0-20%, 20-40% and 40-80% centrality classes.

Quantitative workflow based on NN for weighting criteria in landfill suitability mapping

Science.gov (United States)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Alkhasawneh, Mutasem Sh.; Aziz, Hamidi Abdul

2017-10-01

Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form suitability maps at the regional scale for solid waste planning based on NNs. A feed-forward neural network employed in the workflow. A total of 34 criteria were pre-processed to establish the input dataset for NN modelling. The final learned network used to acquire the weights of the criteria. Accuracies of 95.2% and 93.2% achieved for the training dataset and testing dataset, respectively. The workflow was found to be capable of reducing human interference to generate highly reliable maps. The proposed workflow reveals the applicability of NN in generating landfill suitability maps and the feasibility of integrating them with existing MCDA workflows.

A Hybrid 3D Indoor Space Model

Directory of Open Access Journals (Sweden)

A. Jamali

2016-10-01

Full Text Available GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM, Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.

Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization

Directory of Open Access Journals (Sweden)

Lei La

2012-01-01

Full Text Available AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM, neural networks (NN, naïve Bayes, and k-nearest neighbor (kNN. This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost. We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

Resonance particle production in inelastic N-N and π-N interactions

International Nuclear Information System (INIS)

Amelin, N.S.; Barashenkov, V.S.; Slavin, N.V.

1984-01-01

Using the considerations connected with the scaling hypothesis and the Regge pole model the phenomenological expressions are obtained for differential single-particle inclusive production cross sections (Δ + , Δ 0 , Δ ++ ) and (p +- , p 0 , ω) resonances in inelastic π-N and N-N collisions at high energies. These expressions describe the known experimental data in a wide region of kinematic variables from 10 to several thousand GeV

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

Science.gov (United States)

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.

HyLTL: a temporal logic for model checking hybrid systems

Directory of Open Access Journals (Sweden)

Davide Bresolin

2013-08-01

Full Text Available The model-checking problem for hybrid systems is a well known challenge in the scientific community. Most of the existing approaches and tools are limited to safety properties only, or operates by transforming the hybrid system to be verified into a discrete one, thus loosing information on the continuous dynamics of the system. In this paper we present a logic for specifying complex properties of hybrid systems called HyLTL, and we show how it is possible to solve the model checking problem by translating the formula into an equivalent hybrid automaton. In this way the problem is reduced to a reachability problem on hybrid automata that can be solved by using existing tools.

Hybrid dynamics for currency modeling

OpenAIRE

Theodosopoulos, Ted; Trifunovic, Alex

2006-01-01

We present a simple hybrid dynamical model as a tool to investigate behavioral strategies based on trend following. The multiplicative symbolic dynamics are generated using a lognormal diffusion model for the at-the-money implied volatility term structure. Thus, are model exploits information from derivative markets to obtain qualititative properties of the return distribution for the underlier. We apply our model to the JPY-USD exchange rate and the corresponding 1mo., 3mo., 6mo. and 1yr. im...

Measurements on NN → dπ very near threshold. II

International Nuclear Information System (INIS)

Korkmaz, E.; Li, J.; Hutcheon, D.A.; Abegg, R.; Elliott, J.B.; Greeniaus, L.G.; Mack, D.J.; Miller, C.A.; Rodning, N.L.

1991-04-01

We have measured analyzing powers for the reaction pp → dπ + at beam energies 3 and 7 MeV above pion production threshold. With the use of Watson's Theorem to fix phases and effective range estimates of pion d-wave strength, we have been able to determine the NN → dπ s-wave amplitude and the two p-wave amplitudes at these energies. The results are compared to the Faddeev model predictions of Blankleider. (Author) 11 refs., 2 tabs., 11 figs

Optimization of hybrid model on hajj travel

Science.gov (United States)

Cahyandari, R.; Ariany, R. L.; Sukono

2018-03-01

Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.

Search for weakly decaying $\\overline{\\Lambda\\mathrm{n}}$ and $\\Lambda\\Lambda $ exotic bound states in central Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 2.76 TeV

CERN Document Server

Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmed, Ijaz; Ahn, Sang Un; Aimo, Ilaria; Aiola, Salvatore; Ajaz, Muhammad; Akindinov, Alexander; Alam, Sk Noor; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Armesto Perez, Nestor; Arnaldi, Roberta; Aronsson, Tomas; Arsene, Ionut Cristian; Arslandok, Mesut; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Baldisseri, Alberto; Ball, Markus; Baltasar Dos Santos Pedrosa, Fernando; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biswas, Saikat; Bjelogrlic, Sandro; Blanco, Fernando; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botje, Michiel; Botta, Elena; Boettger, Stefan; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Buxton, Jesse Thomas; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Cavicchioli, Costanza; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; D'Erasmo, Ginevra; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Dobrowolski, Tadeusz Antoni; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Engel, Heiko; Erazmus, Barbara Ewa; Erhardt, Filip; Eschweiler, Dominic; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Felea, Daniel; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Ramirez, Andres; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Grosse-Oetringhaus, Jan Fiete; Grossiord, Jean-Yves; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gulkanyan, Hrant; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hanratty, Luke David; Hansen, Alexander; Harris, John William; Hartmann, Helvi; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Heide, Markus Ansgar; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hilden, Timo Eero; Hillemanns, Hartmut; Hippolyte, Boris; Hristov, Peter Zahariev; Huang, Meidana; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Ilkiv, Iryna; Inaba, Motoi; Ionita, Costin; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacholkowski, Adam Wlodzimierz; Jacobs, Peter Martin; Jahnke, Cristiane; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jung, Hyungtaik; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Kamal; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Beomkyu; Kim, Do Won; Kim, Dong Jo; Kim, Hyeonjoong; Kim, Jinsook; Kim, Mimae; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobayashi, Taiyo; Kobdaj, Chinorat; Kofarago, Monika; Kohler, Markus Konrad; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kouzinopoulos, Charalampos; Kovalenko, Vladimir; Kowalski, Marek; Kox, Serge; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kravcakova, Adela; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kucheryaev, Yury; Kugathasan, Thanushan; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kulakov, Igor; Kumar, Jitendra; Lokesh, Kumar; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kushpil, Svetlana; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Legrand, Iosif; Lehnert, Joerg Walter; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loggins, Vera Renee; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Lu, Xianguo; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manceau, Loic Henri Antoine; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martashvili, Irakli; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Minervini, Lazzaro Manlio; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Morando, Maurizio; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Muller, Hans; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Murray, Sean; Musa, Luciano; Musinsky, Jan; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Nattrass, Christine; Nayak, Kishora; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Paola; Paic, Guy; Pajares Vales, Carlos; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Pant, Divyash; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Paul, Biswarup; Pawlak, Tomasz Jan; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Razazi, Vahedeh; Read, Kenneth Francis; Real, Jean-Sebastien; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reicher, Martijn; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rivetti, Angelo; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Romita, Rosa; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salgado Lopez, Carlos Alberto; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Santagati, Gianluca; Sarkar, Debojit; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schuster, Tim Robin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Seeder, Karin Soraya; Seger, Janet Elizabeth; Sekiguchi, Yuko; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Seo, Jeewon; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shangaraev, Artem; Sharma, Ankita; Sharma, Natasha; Shigaki, Kenta; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Soegaard, Carsten; Soltz, Ron Ariel; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stefanek, Grzegorz; Steinpreis, Matthew Donald; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Sultanov, Rishat; Sumbera, Michal; Symons, Timothy; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Takahashi, Jun; Tanaka, Naoto; Tangaro, Marco-Antonio; Tapia Takaki, Daniel Jesus; Tarantola Peloni, Attilio; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Wang, Yifei; Watanabe, Daisuke; Weber, Michael; Weber, Steffen Georg; Wessels, Johannes Peter; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yaldo, Chris G; Yamaguchi, Yorito; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yasnopolskiy, Stanislav; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym

2016-01-10

We present results of a search for two hypothetical strange dibaryon states, i.e. the H-dibaryon and the possible $\\overline{\\Lambda\\mathrm{n}}$ bound state. The search is performed with the ALICE detector in central (0-10%) Pb-Pb collisions at $ \\sqrt{s_{\\rm{NN}}} = 2.76$ TeV, by invariant mass analysis in the decay modes $\\overline{\\Lambda\\mathrm{n}} \\rightarrow \\overline{\\mathrm{d}} \\pi^{+} $ and H-dibaryon $\\rightarrow \\Lambda \\mathrm{p} \\pi^{-}$. No evidence for these bound states is observed. Upper limits are determined at 99% confidence level for a wide range of lifetimes and for the full range of branching ratios. The results are compared to thermal, coalescence and hybrid UrQMD model expectations, which describe correctly the production of other loosely bound states, like the deuteron and the hypertriton.

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

Directory of Open Access Journals (Sweden)

L. Adamczyk

2015-11-01

Full Text Available 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 0.4NN=17.3 GeV. For sNN=200 GeV, the normalized excess yield in central collisions is higher than that at sNN=17.3 GeV and increases from peripheral to central collisions. These measurements indicate that the lifetime of the hot, dense medium created in central Au+Au collisions at sNN=200 GeV is longer than those in peripheral collisions and at lower energies.

Superconductivity in the periodic Anderson model with anisotropic hybridization

International Nuclear Information System (INIS)

Sarasua, L.G.; Continentino, Mucio A.

2003-01-01

In this work we study superconductivity in the periodic Anderson model with both on-site and intersite hybridization, including the interband Coulomb repulsion. We show that the presence of the intersite hybridization together with the on-site hybridization significantly affects the superconducting properties of the system. The symmetry of the hybridization has a strong influence in the symmetry of the superconducting order parameter of the ground state. The interband Coulomb repulsion may increase or decrease the superconducting critical temperature at small values of this interaction, while is detrimental to superconductivity for strong values. We show that the present model can give rise to positive or negative values of dT c /dP, depending on the values of the system parameters

Ratios of charged antiparticles to particles near midrapidity in Au+Au collisions at (sNN)=200 GeV

Science.gov (United States)

Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Ballintijn, M.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; Garcia, 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.; Michałowski, J.; 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.; Steadman, S. G.; Steinberg, P.; Stephans, G. S.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2003-02-01

The ratios of charged antiparticles to particles have been obtained for pions, kaons, and protons near midrapidity in central Au+Au collisions at (sNN)=200 GeV. Ratios of /=1.025±0.006(stat.)±0.018(syst.), /=0.95±0.03(stat.)±0.03(syst.), and / =0.73±0.02(stat.)±0.03(syst.) have been observed. The / and / ratios are consistent with a baryochemical potential μB of 27 MeV, roughly a factor of 2 smaller than in (sNN)=130 GeV collisions. The data are compared to results from lower energies and model calculations. Our accurate measurements of the particle ratios impose stringent constraints on current and future models dealing with baryon production and transport.

Daily air quality index forecasting with hybrid models: A case in China

International Nuclear Information System (INIS)

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-01-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the

Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud

Directory of Open Access Journals (Sweden)

Jun Hong

2017-01-01

Full Text Available As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate k-nearest neighbors (kNN query and kNN query authentication on an encrypted outsourced spatial database. An asymmetric scalar-product-preserving encryption scheme is elucidated, in which data points and query points are encrypted with diverse encryption keys, and the CSP can determine the distance relation between encrypted data points and query points. Furthermore, the similarity search tree is extended to build a novel verifiable SS-tree that supports efficient kNN query and kNN query verification. It is indicated from the security analysis and experiment results that our scheme not only maintains the confidentiality of outsourced confidential data and query points but also has a lower kNN query processing and verification overhead than the MR-tree.

Hybrid model for simulation of plasma jet injection in tokamak

Science.gov (United States)

Galkin, Sergei A.; Bogatu, I. N.

2016-10-01

Hybrid kinetic model of plasma treats the ions as kinetic particles and the electrons as charge neutralizing massless fluid. The model is essentially applicable when most of the energy is concentrated in the ions rather than in the electrons, i.e. it is well suited for the high-density hyper-velocity C60 plasma jet. The hybrid model separates the slower ion time scale from the faster electron time scale, which becomes disregardable. That is why hybrid codes consistently outperform the traditional PIC codes in computational efficiency, still resolving kinetic ions effects. We discuss 2D hybrid model and code with exact energy conservation numerical algorithm and present some results of its application to simulation of C60 plasma jet penetration through tokamak-like magnetic barrier. We also examine the 3D model/code extension and its possible applications to tokamak and ionospheric plasmas. The work is supported in part by US DOE DE-SC0015776 Grant.

CONTROLLED CONDENSATION IN K-NN AND ITS APPLICATION FOR REAL TIME COLOR IDENTIFICATION

Directory of Open Access Journals (Sweden)

Carmen Villar Patiño

2017-04-01

Full Text Available k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging.

Hypernuclear weak decay experiments at KEK: n-n and n-p coincidence measurement

International Nuclear Information System (INIS)

Outa, H.; Ajimura, S.; Aoki, K.; Banu, A.; Bhang, H.C.; Fukuda, T.; Hashimoto, O.; Hwang, J.I.; Kameoka, S.; Kang, B.H.; Kim, E.H.; Kim, J.H.; Kim, M.J.; Maruta, T.; Miura, Y.; Miyake, Y.; Nagae, T.; Nakamura, M.; Nakamura, S.N.; Noumi, H.; Okada, S.; Okayasu, Y.; Park, H.; Saha, P.K.; Sato, Y.; Sekimoto, M.; Takahashi, T.; Tamura, H.; Tanida, K.; Toyoda, A.; Tsukada, K.; Watanabe, T.; Yim, H.J.

2005-01-01

We performed a coincidence measurement of two nucleons emitted from the nonmesonic weak decay (NMWD) of 5 Λ He and 12 Λ C formed via the (π+,K+) reaction. In both of n+p and n+n pair coincidence spectra, we observed a clean back-to-back correlation coming from the two-body decay of Λp->np and Λn->nn, respectively. We obtained the ratio of the nucleon pair numbers, Nnn/Nnp ( 5 Λ He)=0.45-bar +/--bar 0.11-bar (stat)-bar +/--bar 0.03-bar (syst) in the kinematic region of cosθNN-0.8. Since each decay mode was exclusively detected, the measured ratio should be close to the ratio of Γ(Λp->nn)/Γ(Λn->np). The Γn/Γp ratio was measured also for the NMWD of 12 Λ C. It is also close to 0.5. Those ratios are consistent with recent theoretical calculations based on the heavy meson/direct quark exchange picture

Hybrid Energy System Modeling in Modelica

Energy Technology Data Exchange (ETDEWEB)

William R. Binder; Christiaan J. J. Paredis; Humberto E. Garcia

2014-03-01

In this paper, a Hybrid Energy System (HES) configuration is modeled in Modelica. Hybrid Energy Systems (HES) have as their defining characteristic the use of one or more energy inputs, combined with the potential for multiple energy outputs. Compared to traditional energy systems, HES provide additional operational flexibility so that high variability in both energy production and consumption levels can be absorbed more effectively. This is particularly important when including renewable energy sources, whose output levels are inherently variable, determined by nature. The specific HES configuration modeled in this paper include two energy inputs: a nuclear plant, and a series of wind turbines. In addition, the system produces two energy outputs: electricity and synthetic fuel. The models are verified through simulations of the individual components, and the system as a whole. The simulations are performed for a range of component sizes, operating conditions, and control schemes.

Reactor systems modeling for ICF hybrids

International Nuclear Information System (INIS)

Berwald, D.H.; Meier, W.R.

1980-10-01

The computational models of ICF reactor subsystems developed by LLNL and TRW are described and a computer program was incorporated for use in the EPRI-sponsored Feasibility Assessment of Fusion-Fission Hybrids. Representative parametric variations have been examined. Many of the ICF subsystem models are very preliminary and more quantitative models need to be developed and included in the code

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

Science.gov (United States)

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 NN} = 17.3 GeV. For √{sNN} = 200 GeV, the normalized excess yield in central collisions is higher than that at √{sNN} = 17.3 GeV and increases from peripheral to central collisions. These measurements indicate that the lifetime of the hot, dense medium created in central Au +Au collisions at √{sNN} = 200 GeV is longer than those in peripheral collisions and at lower energies.

Inclusive J/$\\psi$ production in Xe-Xe collisions at $\\sqrt{s_{NN}}$ = 5.44 TeV

CERN Document Server

Acharya, Shreyasi; The ALICE collaboration; Adamova, Dagmar; Adolfsson, Jonatan; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Al-turany, Mohammad; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Ali, Yasir; Alici, Andrea; Alkin, Anton; Alme, Johan; Alt, Torsten; Altenkamper, Lucas; Altsybeev, Igor; Anaam, Mustafa Naji; Andrei, Cristian; Andreou, Dimitra; Andrews, Harry Arthur; Andronic, Anton; Angeletti, Massimo; Anguelov, Venelin; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Anwar, Rafay; Apadula, Nicole; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Baldisseri, Alberto; Ball, Markus; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barioglio, Luca; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartsch, Esther; Bastid, Nicole; Basu, Sumit; Batigne, Guillaume; Batyunya, Boris; Batzing, Paul Christoph; Bazo Alba, Jose Luis; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Espinoza Beltran, Lucina Gabriela; Belyaev, Vladimir; Bencedi, Gyula; Beole, Stefania; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhaduri, Partha Pratim; Bhasin, Anju; Bhat, Inayat Rasool; Bhatt, Himani; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Antonio; Bianchi, Livio; Bianchi, Nicola; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Boca, Gianluigi; Bock, Friederike; Bogdanov, Alexey; Boldizsar, Laszlo; Bombara, Marek; Bonomi, Germano; Bonora, Matthias; Borel, Herve; Borissov, Alexander; Borri, Marcello; Botta, Elena; Bourjau, Christian; Bratrud, Lars; Braun-munzinger, Peter; Bregant, Marco; Broker, Theo Alexander; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buhler, Paul; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Caines, Helen Louise; Caliva, Alberto; Calvo Villar, Ernesto; Soto Camacho, Rabi; Camerini, Paolo; Capon, Aaron Allan; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Chandra, Sinjini; Chang, Beomsu; Chang, Wan; Chapeland, Sylvain; Chartier, Marielle; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Chowdhury, Tasnuva; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Concas, Matteo; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Costanza, Susanna; Crkovska, Jana; Crochet, Philippe; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Dani, Sanskruti; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Derradi De Souza, Rafael; Franz Degenhardt, Hermann; Deisting, Alexander; Deloff, Andrzej; Delsanto, Silvia; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Ruzza, Benedetto; Arteche Diaz, Raul; Dietel, Thomas; Dillenseger, Pascal; Ding, Yanchun; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Van Doremalen, Lennart Vincent; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dudi, Sandeep; Duggal, Ashpreet Kaur; Dukhishyam, Mallick; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erhardt, Filip; Ersdal, Magnus Rentsch; Espagnon, Bruno; Eulisse, Giulio; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Fabbietti, Laura; Faggin, Mattia; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Fernandez Tellez, Arturo; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiorenza, Gabriele; Flor, Fernando; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Francisco, Audrey; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gajdosova, Katarina; Gallio, Mauro; Duarte Galvan, Carlos; Ganoti, Paraskevi; Garabatos Cuadrado, Jose; Garcia-solis, Edmundo Javier; Garg, Kunal; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; De Leone Gay, Maria Beatriz; Germain, Marie; Ghosh, Jhuma; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Greiner, Leo Clifford; Grelli, Alessandro; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Gronefeld, Julius Maximilian; Grosa, Fabrizio; Grosse-oetringhaus, Jan Fiete; Grosso, Raffaele; Guernane, Rachid; Guerzoni, Barbara; Guittiere, Manuel; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Bautista Guzman, Irais; Haake, Rudiger; Habib, Michael Karim; Hadjidakis, Cynthia Marie; Hamagaki, Hideki; Hamar, Gergoe; Hamid, Mohammed; Hamon, Julien Charles; Hannigan, Ryan; Haque, Md Rihan; Harris, John William; Harton, Austin Vincent; Hassan, Hadi; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Gonzalez Hernandez, Emma; Herrera Corral, Gerardo Antonio; Herrmann, Florian; Hetland, Kristin Fanebust; Hilden, Timo Eero; Hillemanns, Hartmut; Hills, Christopher; Hippolyte, Boris; Hohlweger, Bernhard; Horak, David; Hornung, Sebastian; Hosokawa, Ritsuya; Hota, Jyotishree; Hristov, Peter Zahariev; Huang, Chun-lu; Hughes, Charles; Huhn, Patrick; Humanic, Thomas; Hushnud, Hushnud; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Iddon, James Philip; Iga Buitron, Sergio Arturo; Ilkaev, Radiy; Inaba, Motoi; Ippolitov, Mikhail; Islam, Md Samsul; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacak, Barbara; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jaelani, Syaefudin; Jahnke, Cristiane; Jakubowska, Monika Joanna; Janik, Malgorzata Anna; Jena, Chitrasen; Jercic, Marko; Jevons, Oliver; Jimenez Bustamante, Raul Tonatiuh; Jin, Muqing; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karczmarczyk, Przemyslaw; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Ketzer, Bernhard Franz; Khabanova, Zhanna; Khan, Ahsan Mehmood; Khan, Shaista; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Khatun, Anisa; Khuntia, Arvind; Kielbowicz, Miroslaw Marek; Kileng, Bjarte; Kim, Byungchul; Kim, Daehyeok; Kim, Dong Jo; Kim, Eun Joo; Kim, Hyeonjoong; Kim, Jinsook; Kim, Jiyoung; Kim, Minjung; Kim, Se Yong; Kim, Taejun; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Varga-kofarago, Monika; Kohler, Markus Konrad; Kollegger, Thorsten; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Konyushikhin, Maxim; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Kralik, Ivan; Kravcakova, Adela; Kreis, Lukas; Krivda, Marian; Krizek, Filip; Kruger, Mario; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kundu, Sourav; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kushpil, Svetlana; Kvapil, Jakub; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Lai, Yue Shi; Lakomov, Igor; Langoy, Rune; Lapidus, Kirill; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Larionov, Pavel; Laudi, Elisa; Lavicka, Roman; Lea, Ramona; Leardini, Lucia; Lee, Seongjoo; Lehas, Fatiha; Lehner, Sebastian; Lehrbach, Johannes; Lemmon, Roy Crawford; Leon Monzon, Ildefonso; Levai, Peter; Li, Xiaomei; Li, Xing Long; Lien, Jorgen Andre; Lietava, Roman; Lim, Bong-hwi; Lindal, Svein; Lindenstruth, Volker; Lindsay, Scott William; Lippmann, Christian; Lisa, Michael Annan; Litichevskyi, Vladyslav; Liu, Alwina; Ljunggren, Hans Martin; Llope, William; Lodato, Davide Francesco; Loginov, Vitaly; Loizides, Constantinos; Loncar, Petra; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Luhder, Jens Robert; Lunardon, Marcello; Luparello, Grazia; Lupi, Matteo; Maevskaya, Alla; Mager, Magnus; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Malik, Qasim Waheed; Malinina, Liudmila; Mal'kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martinengo, Paolo; Martinez, Jacobb Lee; Martinez Hernandez, Mario Ivan; Martinez-garcia, Gines; Martinez Pedreira, Miguel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Masson, Erwann; Mastroserio, Annalisa; Mathis, Andreas Michael; Toledo Matuoka, Paula Fernanda; Matyja, Adam Tomasz; Mayer, Christoph; Mazzilli, Marianna; Mazzoni, Alessandra Maria; Meddi, Franco; Melikyan, Yuri; Menchaca-rocha, Arturo Alejandro; Meninno, Elisa; Mercado-perez, Jorge; Meres, Michal; Soncco Meza, Carlos; Mhlanga, Sibaliso; Miake, Yasuo; Micheletti, Luca; Mieskolainen, Matti Mikael; Mihaylov, Dimitar Lubomirov; Mikhaylov, Konstantin; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Auro Prasad; Mohanty, Bedangadas; Khan, Mohammed Mohisin; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munning, Konstantin; Arratia Munoz, Miguel Ignacio; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Myers, Corey James; Myrcha, Julian Wojciech; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Narayan, Amrendra; Naru, Muhammad Umair; Nassirpour, Adrian Fereydon; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Negrao De Oliveira, Renato Aparecido; Nellen, Lukas; Nesbo, Simon Voigt; Neskovic, Gvozden; Ng, Fabian; Nicassio, Maria; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oh, Hoonjung; Ohlson, Alice Elisabeth; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Pachmayer, Yvonne Chiara; Pacik, Vojtech; Pagano, Davide; Paic, Guy; Palni, Prabhakar; Pan, Jinjin; Pandey, Ashutosh Kumar; Panebianco, Stefano; Papikyan, Vardanush; Pareek, Pooja; Park, Jonghan; Parkkila, Jasper Elias; Parmar, Sonia; Passfeld, Annika; Pathak, Surya Prakash; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Peng, Xinye; Pereira, Luis Gustavo; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrovici, Mihai; Petta, Catia; Peretti Pezzi, Rafael; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Pisano, Silvia; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pliquett, Fabian; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Poppenborg, Hendrik; Porteboeuf, Sarah Julie; Pozdniakov, Valeriy; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Punin, Valery; Putschke, Jorn Henning; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Ratza, Viktor; Ravasenga, Ivan; Read, Kenneth Francis; Redlich, Krzysztof; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reshetin, Andrey; Revol, Jean-pierre; Reygers, Klaus Johannes; Riabov, Viktor; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-lucian; Rode, Sudhir Pandurang; Rodriguez Cahuantzi, Mario; Roeed, Ketil; Rogalev, Roman; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Rokita, Przemyslaw Stefan; Ronchetti, Federico; Dominguez Rosas, Edgar; Roslon, Krystian; Rosnet, Philippe; Rossi, Andrea; Rotondi, Alberto; Roukoutakis, Filimon; Roy, Christelle Sophie; Roy, Pradip Kumar; Vazquez Rueda, Omar; Rui, Rinaldo; Rumyantsev, Boris; Rustamov, Anar; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Saha, Sumit Kumar; Sahoo, Baidyanath; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandoval, Andres; Sarkar, Amal; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Sas, Mike Henry Petrus; Scapparone, Eugenio; Scarlassara, Fernando; Schaefer, Brennan; Scheid, Horst Sebastian; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schmidt, Marten Ole; Schmidt, Martin; Schmidt, Nicolas Vincent; Schukraft, Jurgen; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sett, Priyanka; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shahoyan, Ruben; Shaikh, Wadut; Shangaraev, Artem; Sharma, Anjali; Sharma, Ankita; Sharma, Meenakshi; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shimomura, Maya; Shirinkin, Sergey; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singh, Randhir; Singhal, Vikas; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Soramel, Francesca; Sorensen, Soren Pontoppidan; Sozzi, Federica; Sputowska, Iwona Anna; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Stocco, Diego; Storetvedt, Maksim Melnik; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Suzuki, Ken; Swain, Sagarika; Szabo, Alexander; Szarka, Imrich; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terrevoli, Cristina; Teyssier, Boris; Thakur, Dhananjaya; Thakur, Sanchari; Thomas, Deepa; Thoresen, Freja; Tieulent, Raphael Noel; Tikhonov, Anatoly; Timmins, Anthony Robert; Toia, Alberica; Topilskaya, Nataliya; Toppi, Marco; Rojas Torres, Solangel; Tripathy, Sushanta; Trogolo, Stefano; Trombetta, Giuseppe; Tropp, Lukas; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Trzcinski, Tomasz Piotr; Trzeciak, Barbara Antonina; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Umaka, Ejiro Naomi; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vazquez Doce, Oton; Vechernin, Vladimir; Veen, Annelies Marianne; Vercellin, Ermanno; Vergara Limon, Sergio; Vermunt, Luuk; Vernet, Renaud; Vertesi, Robert; Vickovic, Linda; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Virgili, Tiziano; Vislavicius, Vytautas; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Voscek, Dominik; Vranic, Danilo; Vrlakova, Janka; Wagner, Boris; Wang, Hongkai; Wang, Mengliang; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Wegrzynek, Adam; Weiser, Dennis Franz; Wenzel, Sandro Christian; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Willems, Guido Alexander; Williams, Crispin; Willsher, Emily; Windelband, Bernd Stefan; Witt, William Edward; Xu, Ran; Yalcin, Serpil; Yamakawa, Kosei; Yano, Satoshi; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correa Zanoli, Henrique Jose; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zhalov, Mikhail; Zhang, Xiaoming; Zhang, Yonghong; Zhang, Zuman; Zhao, Chengxin; Zherebchevskii, Vladimir; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Ya; Zichichi, Antonino; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zmeskal, Johann; Zou, Shuguang

2018-01-01

Inclusive J/$\\psi$ production is studied in Xe-Xe interactions at a centre-of-mass energy per nucleon pair of $\\sqrt{s_{NN}}$ = 5.44 TeV, using the ALICE detector at the CERN LHC. The J/$\\psi$ meson is reconstructed via its decay into a muon pair, in the centre-of-mass rapidity interval 2.5 < y < 4 and down to zero transverse momentum. In this Letter, the nuclear modification factors $R_{AA}$ for inclusive J/$\\psi$, measured in the centrality range 0-90% as well as in the centrality intervals 0-20% and 20-90% are presented. The $R_{AA}$ values are compared to previously published results for Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV and to the calculation of a transport model. A good agreement is found between Xe-Xe and Pb-Pb results as well as between data and the model.

Stochastic linear hybrid systems: Modeling, estimation, and application

Science.gov (United States)

Seah, Chze Eng

Hybrid systems are dynamical systems which have interacting continuous state and discrete state (or mode). Accurate modeling and state estimation of hybrid systems are important in many applications. We propose a hybrid system model, known as the Stochastic Linear Hybrid System (SLHS), to describe hybrid systems with stochastic linear system dynamics in each mode and stochastic continuous-state-dependent mode transitions. We then develop a hybrid estimation algorithm, called the State-Dependent-Transition Hybrid Estimation (SDTHE) algorithm, to estimate the continuous state and discrete state of the SLHS from noisy measurements. It is shown that the SDTHE algorithm is more accurate or more computationally efficient than existing hybrid estimation algorithms. Next, we develop a performance analysis algorithm to evaluate the performance of the SDTHE algorithm in a given operating scenario. We also investigate sufficient conditions for the stability of the SDTHE algorithm. The proposed SLHS model and SDTHE algorithm are illustrated to be useful in several applications. In Air Traffic Control (ATC), to facilitate implementations of new efficient operational concepts, accurate modeling and estimation of aircraft trajectories are needed. In ATC, an aircraft's trajectory can be divided into a number of flight modes. Furthermore, as the aircraft is required to follow a given flight plan or clearance, its flight mode transitions are dependent of its continuous state. However, the flight mode transitions are also stochastic due to navigation uncertainties or unknown pilot intents. Thus, we develop an aircraft dynamics model in ATC based on the SLHS. The SDTHE algorithm is then used in aircraft tracking applications to estimate the positions/velocities of aircraft and their flight modes accurately. Next, we develop an aircraft conformance monitoring algorithm to detect any deviations of aircraft trajectories in ATC that might compromise safety. In this application, the SLHS

Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

Science.gov (United States)

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; Dubla, A; Ducroux, L; Dupieux, P; Ehlers, R J; Elia, D; Endress, E; Engel, H; Epple, E; Erazmus, B; Erdemir, I; Erhardt, F; 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; 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; Fronze, G G; 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; Gauger, E F; 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; Gonzalez, V; González-Zamora, P; Gorbunov, S; 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; Reed, R J; Rehman, A; Reichelt, P; Reidt, F; Ren, X; Renfordt, R; Reolon, A R; Reshetin, A; Revol, J-P; Reygers, K; Riabov, V; Ricci, R A; Richert, T; Richter, M; Riedler, P; Riegler, W; Riggi, F; Ristea, C; 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; Salzwedel, J; Sambyal, S; Samsonov, V; Šándor, L; Sandoval, A; Sano, M; Sarkar, D; Sarma, P; Scapparone, E; Scarlassara, F; 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; 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, 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; Song, J; Song, M; Song, Z; Soramel, F; Sorensen, S; de Souza, R D; Sozzi, F; Spacek, M; Spiriti, E; Sputowska, I; Spyropoulou-Stassinaki, M; Stachel, J; Stan, I; Stankus, P; Stefanek, G; 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; Szabo, A; Szanto de Toledo, A; Szarka, I; Szczepankiewicz, A; Szymanski, M; Tabassam, U; Takahashi, J; Tambave, G J; Tanaka, N; Tangaro, M 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; 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; 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; Villatoro Tello, A; 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; 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; 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.

Fluid Petri Nets and hybrid model-checking: a comparative case study

International Nuclear Information System (INIS)

Gribaudo, M.; Horvath, A.; Bobbio, A.; Tronci, E.; Ciancamerla, E.; Minichino, M.

2003-01-01

The modeling and analysis of hybrid systems is a recent and challenging research area which is actually dominated by two main lines: a functional analysis based on the description of the system in terms of discrete state (hybrid) automata (whose goal is to ascertain conformity and reachability properties), and a stochastic analysis (whose aim is to provide performance and dependability measures). This paper investigates a unifying view between formal methods and stochastic methods by proposing an analysis methodology of hybrid systems based on Fluid Petri Nets (FPNs). FPNs can be analyzed directly using appropriate tools. Our paper shows that the same FPN model can be fed to different functional analyzers for model checking. In order to extensively explore the capability of the technique, we have converted the original FPN into languages for discrete as well as hybrid as well as stochastic model checkers. In this way, a first comparison among the modeling power of well known tools can be carried out. Our approach is illustrated by means of a 'real world' hybrid system: the temperature control system of a co-generative plant

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

Science.gov (United States)

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.

Model Predictive Control for Connected Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Kaijiang Yu

2015-01-01

Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

Science.gov (United States)

Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

2017-01-01

To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

HYbrid Coordinate Ocean Model (HYCOM): Global

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Global HYbrid Coordinate Ocean Model (HYCOM) and U.S. Navy Coupled Ocean Data Assimilation (NCODA) 3-day, daily forecast at approximately 9-km (1/12-degree)...

A hybrid personalized data recommendation approach for geoscience data sharing

Science.gov (United States)

WANG, M.; Wang, J.

2016-12-01

Recommender systems are effective tools helping Internet users overcome information overloading. The two most widely used recommendation algorithms are collaborating filtering (CF) and content-based filtering (CBF). A number of recommender systems based on those two algorithms were developed for multimedia, online sells, and other domains. Each of the two algorithms has its advantages and shortcomings. Hybrid approaches that combine these two algorithms are better choices in many cases. In geoscience data sharing domain, where the items (datasets) are more informative (in space and time) and domain-specific, no recommender system is specialized for data users. This paper reports a dynamic weighted hybrid recommendation algorithm that combines CF and CBF for geoscience data sharing portal. We first derive users' ratings on items with their historical visiting time by Jenks Natural Break. In the CBF part, we incorporate the space, time, and subject information of geoscience datasets to compute item similarity. Predicted ratings were computed with k-NN method separately using CBF and CF, and then combined with weights. With training dataset we attempted to find the best model describing ideal weights and users' co-rating numbers. A logarithmic function was confirmed to be the best model. The model was then used to tune the weights of CF and CBF on user-item basis with test dataset. Evaluation results show that the dynamic weighted approach outperforms either solo CF or CBF approach in terms of Precision and Recall.

A Hybrid Wind-Farm Parametrization for Mesoscale and Climate Models

Science.gov (United States)

Pan, Yang; Archer, Cristina L.

2018-04-01

To better understand the potential impact of wind farms on weather and climate at the regional to global scales, a new hybrid wind-farm parametrization is proposed for mesoscale and climate models. The proposed parametrization is a hybrid model because it is not based on physical processes or conservation laws, but on the multiple linear regression of the results of large-eddy simulations (LES) with the geometric properties of the wind-farm layout (e.g., the blockage ratio and blockage distance). The innovative aspect is that each wind turbine is treated individually based on its position in the farm and on the wind direction by predicting the velocity upstream of each turbine. The turbine-induced forces and added turbulence kinetic energy (TKE) are first derived analytically and then implemented in the Weather Research and Forecasting model. Idealized simulations of the offshore Lillgrund wind farm are conducted. The wind-speed deficit and TKE predicted with the hybrid model are in excellent agreement with those from the LES results, while the wind-power production estimated with the hybrid model is within 10% of that observed. Three additional wind farms with larger inter-turbine spacing than at Lillgrund are also considered, and a similar agreement with LES results is found, proving that the hybrid parametrization works well with any wind farm regardless of the spacing between turbines. These results indicate the wind-turbine position, wind direction, and added TKE are essential in accounting for the wind-farm effects on the surroundings, for which the hybrid wind-farm parametrization is a promising tool.

Rapidity distributions of hadrons in the HydHSD hybrid model

Energy Technology Data Exchange (ETDEWEB)

Khvorostukhin, A. S., E-mail: hvorost@theor.jinr.ru; Toneev, V. D. [Joint Institute for Nuclear Research (Russian Federation)

2017-03-15

A multistage hybrid model intended for describing heavy-ion interactions in the energy region of the NICA collider under construction in Dubna is proposed. The model combines the initial, fast, interaction stage described by the model of hadron string dynamics (HSD) and the subsequent evolution that the expanding system formed at the first stage experiences at the second stage and which one treats on the basis of ideal hydrodynamics; after the completion of the second stage, the particles involved may still undergo rescattering (third interaction stage). The model admits three freeze-out scenarios: isochronous, isothermal, and isoenergetic. Generally, the HydHSD hybrid model developed in the present study provides fairly good agreement with available experimental data on proton rapidity spectra. It is shown that, within this hybrid model, the two-humped structure of proton rapidity distributions can be obtained either by increasing the freeze-out temperature and energy density or by more lately going over to the hydrodynamic stage. Although the proposed hybrid model reproduces rapidity spectra of protons, it is unable to describe rapidity distributions of pions, systematically underestimating their yield. It is necessary to refine the model by including viscosity effects at the hydrodynamic stage of evolution of the system and by considering in more detail the third interaction stage.

Pseudorapidity density of charged particles p-Pb collisions at $\\sqrt{s_{NN}}$ = 5.02 TeV

CERN Document Server

Abelev, Betty; Adamova, Dagmar; Adare, Andrew Marshall; Aggarwal, Madan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agocs, Andras Gabor; Agostinelli, Andrea; Ahammed, Zubayer; Ahmad, Nazeer; Ahmad, Arshad; Ahn, Sang Un; Ahn, Sul-Ah; Ajaz, Muhammad; Akindinov, Alexander; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Almaraz Avina, Erick Jonathan; Alme, Johan; Alt, Torsten; Altini, Valerio; Altinpinar, Sedat; Altsybeev, Igor; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshauser, Harald; Arbor, Nicolas; Arcelli, Silvia; Arend, Andreas; Armesto, Nestor; Arnaldi, Roberta; Aronsson, Tomas Robert; Arsene, Ionut Cristian; Arslandok, Mesut; Asryan, Andzhey; Augustinus, Andre; Averbeck, Ralf Peter; Awes, Terry; Aysto, Juha Heikki; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bailhache, Raphaelle Marie; Bala, Renu; Baldini Ferroli, Rinaldo; Baldisseri, Alberto; Baltasar Dos Santos Pedrosa, Fernando; Ban, Jaroslav; Baral, Rama Chandra; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Barret, Valerie; Bartke, Jerzy Gustaw; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batyunya, Boris; Baumann, Christoph Heinrich; Bearden, Ian Gardner; Beck, Hans; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bellwied, Rene; Belmont-Moreno, Ernesto; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bergognon, Anais Annick Erica; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhati, Ashok Kumar; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Bjelogrlic, Sandro; Blanco, Francesco; Blanco, F; Blau, Dmitry; Blume, Christoph; Boccioli, Marco; Boettger, Stefan; Bogdanov, Alexey; Boggild, Hans; Bogolyubsky, Mikhail; Boldizsar, Laszlo; Bombara, Marek; Book, Julian; Borel, Herve; Borissov, Alexander; Bossu, Francesco; Botje, Michiel; Botta, Elena; Braidot, Ermes; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Browning, Tyler Allen; Broz, Michal; Brun, Rene; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Busch, Oliver; Buthelezi, Edith Zinhle; Caballero Orduna, Diego; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calvo Villar, Ernesto; Camerini, Paolo; Canoa Roman, Veronica; Cara Romeo, Giovanni; Carena, Wisla; Carena, Francesco; Carlin Filho, Nelson; Carminati, Federico; Casanova Diaz, Amaya Ofelia; Castillo Castellanos, Javier Ernesto; Castillo Hernandez, Juan Francisco; Casula, Ester Anna Rita; Catanescu, Vasile; Cavicchioli, Costanza; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Chang, Beomsu; Chapeland, Sylvain; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chawla, Isha; Cherney, Michael Gerard; Cheshkov, Cvetan; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Chinellato, David; Chochula, Peter; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Coccetti, Fabrizio; Colamaria, Fabio; Colella, Domenico; Collu, Alberto; Conesa Balbastre, Gustavo; Conesa del Valle, Zaida; Connors, Megan Elizabeth; Contin, Giacomo; Contreras, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortese, Pietro; Cortes Maldonado, Ismael; Cosentino, Mauro Rogerio; Costa, Filippo; Cotallo, Manuel Enrique; Crescio, Elisabetta; Crochet, Philippe; Cruz Alaniz, Emilia; Cuautle, Eleazar; Cunqueiro, Leticia; Dainese, Andrea; Dalsgaard, Hans Hjersing; Danu, Andrea; Das, Kushal; Das, Indranil; Das, Supriya; Das, Debasish; Dash, Sadhana; Dash, Ajay Kumar; De, Sudipan; de Barros, Gabriel; De Caro, Annalisa; de Cataldo, Giacinto; de Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; Delagrange, Hugues; Deloff, Andrzej; De Marco, Nora; Denes, Ervin; De Pasquale, Salvatore; Deppman, Airton; D'Erasmo, Ginevra; de Rooij, Raoul Stefan; Diaz Corchero, Miguel Angel; Di Bari, Domenico; Dietel, Thomas; Di Giglio, Carmelo; Di Liberto, Sergio; Di Mauro, Antonio; Di Nezza, Pasquale; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Dobrowolski, Tadeusz Antoni; Donigus, Benjamin; Dordic, Olja; Driga, Olga; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Dutta Majumdar, Mihir Ranjan; Dutta Majumdar, AK; Elia, Domenico; Emschermann, David Philip; Engel, Heiko; Erazmus, Barbara; Erdal, Hege Austrheim; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Evans, David; Eyyubova, Gyulnara; Fabris, Daniela; Faivre, Julien; Falchieri, Davide; Fantoni, Alessandra; Fasel, Markus; Fearick, Roger Worsley; Fehlker, Dominik; Feldkamp, Linus; Felea, Daniel; Feliciello, Alessandro; Fenton-Olsen, Bo; Feofilov, Grigory; Fernandez Tellez, Arturo; Ferretti, Alessandro; Festanti, Andrea; Figiel, Jan; Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fuchs, Ulrich; Furget, Christophe; Fusco Girard, Mario; Gaardhoje, Jens Joergen; Gagliardi, Martino; Gago, Alberto; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Garabatos, Jose; Garcia-Solis, Edmundo; Garishvili, Irakli; Gerhard, Jochen; Germain, Marie; Geuna, Claudio; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Gianotti, Paola; Girard, Martin Robert; Giubellino, Paolo; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez, Ramon; Gonzalez Ferreiro, Elena; Gonzalez-Trueba, Laura Helena; Gonzalez-Zamora, Pedro; Gorbunov, Sergey; Goswami, Ankita; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Grajcarek, Robert; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoriev, Vladislav; Grigoryan, Smbat; Grigoryan, Ara; Grinyov, Boris; Grion, Nevio; Gros, Philippe; Grosse-Oetringhaus, Jan Fiete; Grossiord, Jean-Yves; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerra Gutierrez, Cesar; Guerzoni, Barbara; Guilbaud, Maxime Rene Joseph; Gulbrandsen, Kristjan Herlache; Gulkanyan, Hrant; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Han, Byounghee; Hanratty, Luke David; Hansen, Alexander; Harmanova, Zuzana; Harris, John William; Hartig, Matthias; Harton, Austin; Hasegan, Dumitru; Hatzifotiadou, Despoina; Hayashi, Shinichi; Hayrapetyan, Arsen; Heckel, Stefan Thomas; Heide, Markus Ansgar; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Herrmann, Norbert; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hicks, Bernard; Hippolyte, Boris; Hori, Yasuto; Hristov, Peter Zahariev; Hrivnacova, Ivana; Huang, Meidana; Humanic, Thomas; Hwang, Dae Sung; Ichou, Raphaelle; Ilkaev, Radiy; Ilkiv, Iryna; Inaba, Motoi; Incani, Elisa; Innocenti, Gian Michele; Innocenti, Pier Giorgio; Ippolitov, Mikhail; Irfan, Muhammad; Ivan, Cristian George; Ivanov, Vladimir; Ivanov, Andrey; Ivanov, Marian; Ivanytskyi, Oleksii; Jacholkowski, Adam Wlodzimierz; Jacobs, Peter; Jang, Haeng Jin; Janik, Rudolf; Janik, Malgorzata Anna; Jayarathna, Sandun; Jena, Satyajit; Jha, Deeptanshu Manu; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jung, Hyung Taik; Jusko, Anton; Kaidalov, Alexei; Kalcher, Sebastian; Kalinak, Peter; Kalliokoski, Tuomo Esa Aukusti; Kalweit, Alexander Philipp; Kang, Ju Hwan; Kaplin, Vladimir; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karpechev, Evgeny; Kazantsev, Andrey; Kebschull, Udo Wolfgang; Keidel, Ralf; Khan, Kamal Hussain; Khan, Palash; Khan, Mohisin Mohammed; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Taesoo; Kim, Beomkyu; Kim, Jonghyun; Kim, Jin Sook; Kim, Mimae; Kim, Minwoo; Kim, Se Yong; Kim, Dong Jo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Klay, Jennifer Lynn; Klein, Jochen; Klein-Bosing, Christian; Kliemant, Michael; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kohler, Markus; Kollegger, Thorsten; Kolojvari, Anatoly; Kondratiev, Valery; Kondratyeva, Natalia; Konevskih, Artem; Kour, Ravjeet; Kovalenko, Vladimir; Kowalski, Marek; Kox, Serge; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kramer, Frederick; Kravcakova, Adela; Krawutschke, Tobias; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Krus, Miroslav; Kryshen, Evgeny; Krzewicki, Mikolaj; Kucheriaev, Yury; Kugathasan, Thanushan; Kuhn, Christian Claude; Kuijer, Paul; Kulakov, Igor; Kumar, Jitendra; Kurashvili, Podist; Kurepin, A; Kurepin, AB; Kuryakin, Alexey; Kushpil, Vasily; Kushpil, Svetlana; Kvaerno, Henning; Kweon, Min Jung; Kwon, Youngil; Ladron de Guevara, Pedro; Lakomov, Igor; Langoy, Rune; La Pointe, Sarah Louise; Lara, Camilo Ernesto; Lardeux, Antoine Xavier; La Rocca, Paola; Lea, Ramona; Lechman, Mateusz; Lee, Ki Sang; Lee, Sung Chul; Lee, Graham Richard; Legrand, Iosif; Lehnert, Joerg Walter; Lenhardt, Matthieu Laurent; Lenti, Vito; Leon, Hermes; Leoncino, Marco; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Levai, Peter; Lien, Jorgen; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Loenne, Per-Ivar; Loggins, Vera; Loginov, Vitaly; Lohner, Daniel; Loizides, Constantinos; Loo, Kai Krister; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lovhoiden, Gunnar; Lu, Xianguo; Luettig, Philipp; Lunardon, Marcello; Luo, Jiebin; Luparello, Grazia; Luzzi, Cinzia; Ma, Ke; Ma, Rongrong; Madagodahettige-Don, Dilan Minthaka; Maevskaya, Alla; Mager, Magnus; Mahapatra, Durga Prasad; Maire, Antonin; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Ludmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manceau, Loic Henri Antoine; Mangotra, Lalit Kumar; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martashvili, Irakli; Martin, Nicole Alice; Martinengo, Paolo; Martinez, Mario Ivan; Martinez Davalos, Arnulfo; Martinez Garcia, Gines; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Mastroserio, Annalisa; Matthews, Zoe Louise; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel; Mazzoni, Alessandra Maria; Meddi, Franco; Menchaca-Rocha, Arturo Alejandro; Mercado Perez, Jorge; Meres, Michal; Miake, Yasuo; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz; Mitu, Ciprian Mihai; Mizuno, Sanshiro; Mlynarz, Jocelyn; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Monteno, Marco; Montes, Esther; Moon, Taebong; Morando, Maurizio; Moreira De Godoy, Denise Aparecida; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhuri, Sanjib; Mukherjee, Maitreyee; Muller, Hans; Munhoz, Marcelo; Musa, Luciano; Musso, Alfredo; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Nattrass, Christine; Navin, Sparsh; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nicassio, Maria; Niculescu, Mihai; Nielsen, Borge Svane; Niida, Takafumi; Nikolaev, Sergey; Nikolic, Vedran; Nikulin, Vladimir; Nikulin, Sergey; Nilsen, Bjorn Steven; Nilsson, Mads Stormo; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Novitzky, Norbert; Nyanin, Alexandre; Nyatha, Anitha; Nygaard, Casper; Nystrand, Joakim Ingemar; Ochirov, Alexander; Oeschler, Helmut Oskar; Oh, Sun Kun; Oh, Saehanseul; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oppedisano, Chiara; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Ostrowski, Piotr Krystian; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozawa, Kyoichiro; Pachmayer, Yvonne Chiara; Pachr, Milos; Padilla, Fatima; Pagano, Paola; Paic, Guy; Painke, Florian; Pajares, Carlos; Pal, Susanta Kumar; Palaha, Arvinder Singh; Palmeri, Armando; Papikyan, Vardanush; Pappalardo, Giuseppe; Park, Woo Jin; Passfeld, Annika; Pastircak, Blahoslav; Patalakha, Dmitri Ivanovich; Paticchio, Vincenzo; Paul, Biswarup; Pavlinov, Alexei; Pawlak, Tomasz Jan; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitri; Perez Lara, Carlos Eugenio; Perini, Diego; Perrino, Davide; Peryt, Wiktor Stanislaw; Pesci, Alessandro; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petran, Michal; Petris, Mariana; Petrov, Plamen Rumenov; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Piccotti, Anna; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Pitz, Nora; Piyarathna, Danthasinghe; Planinic, Mirko; Ploskon, Mateusz Andrzej; Pluta, Jan Marian; Pocheptsov, Timur; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polak, Karel; Polichtchouk, Boris; Pop, Amalia; Porteboeuf-Houssais, Sarah; Pospisil, Vladimir; Potukuchi, Baba; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puddu, Giovanna; Punin, Valery; Putis, Marian; Putschke, Jorn Henning; Quercigh, Emanuele; Qvigstad, Henrik; Rachevski, Alexandre; Rademakers, Alphonse; Raiha, Tomi Samuli; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Ramirez Reyes, Abdiel; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Real, Jean-Sebastien; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick; Reicher, Martijn; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riccati, Lodovico; Ricci, Renato Angelo; Richert, Tuva; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roed, Ketil; Rohr, David; Rohrich, Dieter; Romita, Rosa; Ronchetti, Federico; Rosnet, Philippe; Rossegger, Stefan; Rossi, Andrea; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Rybicki, Andrzej; Sadovsky, Sergey; Safarik, Karel; Sahoo, Raghunath; Sahu, Pradip Kumar; Saini, Jogender; Sakaguchi, Hiroaki; Sakai, Shingo; Sakata, Dosatsu; Salgado, Carlos Albert; Salzwedel, Jai; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sano, Satoshi; Santagati, Gianluca; Santoro, Romualdo; Sarkamo, Juho Jaako; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schreiner, Steffen; Schuchmann, Simone; Schukraft, Jurgen; Schuster, Tim; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Patrick Aaron; Scott, Rebecca; Segato, Gianfranco; Selyuzhenkov, Ilya; Senyukov, Serhiy; Seo, Jeewon; Serci, Sergio; Serradilla, Eulogio; Sevcenco, Adrian; Shabetai, Alexandre; Shabratova, Galina; Shahoyan, Ruben; Sharma, Satish; Sharma, Natasha; Sharma, Rohini; Shigaki, Kenta; Shtejer, Katherin; Sibiriak, Yury; Siciliano, Melinda; Siddhanta, Sabyasachi; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Skjerdal, Kyrre; Smakal, Radek; Smirnov, Nikolai; Snellings, Raimond; Sogaard, Carsten; Soltz, Ron Ariel; Son, Hyungsuk; Song, Jihye; Song, Myunggeun; Soos, Csaba; Soramel, Francesca; Sputowska, Iwona; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stan, Ionel; Stefanek, Grzegorz; Steinpreis, Matthew; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Stolpovskiy, Mikhail; Strmen, Peter; Suaide, Alexandre Alarcon do Passo; Subieta Vasquez, Martin Alfonso; Sugitate, Toru; Suire, Christophe Pierre; Sultanov, Rishat; Sumbera, Michal; Susa, Tatjana; Symons, Timothy; Szanto de Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szostak, Artur Krzysztof; Szymanski, Maciej; Takahashi, Jun; Tapia Takaki, Daniel Jesus; Tarantola Peloni, Attilio; Tarazona Martinez, Alfonso; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terrevoli, Cristina; Thader, Jochen Mathias; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony; Tlusty, David; Toia, Alberica; Torii, Hisayuki; Toscano, Luca; Trubnikov, Victor; Truesdale, David Christopher; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ulery, Jason Glyndwr; Ullaland, Kjetil; Ulrich, Jochen; Uras, Antonio; Urban, Jozef; Urciuoli, Guido Marie; Usai, Gianluca; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Vande Vyvre, Pierre; van Leeuwen, Marco; Vannucci, Luigi; Vargas, Aurora Diozcora; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vechernin, Vladimir; Veldhoen, Misha; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Yury; Vinogradov, Leonid; Virgili, Tiziano; Viyogi, Yogendra; Vodopianov, Alexander; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Vladimir; Wagner, Boris; Wan, Renzhuo; Wang, Yaping; Wang, Mengliang; Wang, Dong; Wang, Yifei; Watanabe, Kengo; Weber, Michael; Wessels, Johannes; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Grzegorz Andrzej; Wilk, Alexander; Williams, Crispin; Windelband, Bernd Stefan; Xaplanteris Karampatsos, Leonidas; Yaldo, Chris G; Yamaguchi, Yorito; Yang, Shiming; Yang, Hongyan; Yasnopolsky, Stanislav; Yi, JunGyu; Yin, Zhongbao; Yoo, In-Kwon; Yoon, Jongik; Yu, Weilin; Yuan, Xianbao; Yushmanov, Igor; Zaccolo, Valentina; Zach, Cenek; Zampolli, Chiara; Zaporozhets, Sergey; Zarochentsev, Andrey; Zavada, Petr; Zaviyalov, Nikolai; Zbroszczyk, Hanna Paulina; Zelnicek, Pierre; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhou, Fengchu; Zhou, Daicui; Zhou, You; Zhu, Jianhui; Zhu, Hongsheng; Zhu, Jianlin; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zinovjev, Gennady; Zoccarato, Yannick Denis; Zynovyev, Mykhaylo; Zyzak, Maksym

2013-01-18

The charged-particle pseudorapidity density measured over 4 units of pseudorapidity in non-single-diffractive (NSD) p-Pb collisions at a centre-of-mass energy per nucleon pair $\\sqrt{s_{NN}}$ = 5.02 TeV is presented. The average value at midrapidity is measured to be 16.81 $\\pm$ 0.71 (syst.), which corresponds to 2.14 $\\pm$ 0.17 (syst.) per participating nucleon. This is 16% lower than in NSD pp collisions interpolated to the same collision energy, and 84% higher than in d-Au collisions at $\\sqrt{s_{NN}}$ = 0.2 TeV. The measured pseudorapidity density in p-Pb collisions is compared to model predictions, and provides new constraints on the description of particle production in high-energy nuclear collisions.

Design, analysis and modeling of a novel hybrid powertrain system based on hybridized automated manual transmission

Science.gov (United States)

Wu, Guang; Dong, Zuomin

2017-09-01

Hybrid electric vehicles are widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and lower emissions at competitive costs. In recent years, various hybrid powertrain systems were proposed and implemented based on different types of conventional transmission. Power-split system, including Toyota Hybrid System and Ford Hybrid System, are well-known examples. However, their relatively low torque capacity, and the drive of alternative and more advanced designs encouraged other innovative hybrid system designs. In this work, a new type of hybrid powertrain system based hybridized automated manual transmission (HAMT) is proposed. By using the concept of torque gap filler (TGF), this new hybrid powertrain type has the potential to overcome issue of torque gap during gearshift. The HAMT design (patent pending) is described in details, from gear layout and design of gear ratios (EV mode and HEV mode) to torque paths at different gears. As an analytical tool, mutli-body model of vehicle equipped with this HAMT was built to analyze powertrain dynamics at various steady and transient modes. A gearshift was decomposed and analyzed based basic modes. Furthermore, a Simulink-SimDriveline hybrid vehicle model was built for the new transmission, driveline and vehicle modular. Control strategy has also been built to harmonically coordinate different powertrain components to realize TGF function. A vehicle launch simulation test has been completed under 30% of accelerator pedal position to reveal details during gearshift. Simulation results showed that this HAMT can eliminate most torque gap that has been persistent issue of traditional AMT, improving both drivability and performance. This work demonstrated a new type of transmission that features high torque capacity, high efficiency and improved drivability.

A four-stage hybrid model for hydrological time series forecasting.

Science.gov (United States)

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Science.gov (United States)

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

Hybrid modelling framework by using mathematics-based and information-based methods

International Nuclear Information System (INIS)

Ghaboussi, J; Kim, J; Elnashai, A

2010-01-01

Mathematics-based computational mechanics involves idealization in going from the observed behaviour of a system into mathematical equations representing the underlying mechanics of that behaviour. Idealization may lead mathematical models that exclude certain aspects of the complex behaviour that may be significant. An alternative approach is data-centric modelling that constitutes a fundamental shift from mathematical equations to data that contain the required information about the underlying mechanics. However, purely data-centric methods often fail for infrequent events and large state changes. In this article, a new hybrid modelling framework is proposed to improve accuracy in simulation of real-world systems. In the hybrid framework, a mathematical model is complemented by information-based components. The role of informational components is to model aspects which the mathematical model leaves out. The missing aspects are extracted and identified through Autoprogressive Algorithms. The proposed hybrid modelling framework has a wide range of potential applications for natural and engineered systems. The potential of the hybrid methodology is illustrated through modelling highly pinched hysteretic behaviour of beam-to-column connections in steel frames.

Hybrid Model of Content Extraction

DEFF Research Database (Denmark)

Qureshi, Pir Abdul Rasool; Memon, Nasrullah

2012-01-01

We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict...... significance of the node towards overall content provided by the document. Once significance of the nodes is determined, the formatting characteristics like fonts, styles and the position of the nodes are evaluated to identify the nodes with similar formatting as compared to the significant nodes. The proposed...

Model predictive control of hybrid systems : stability and robustness

NARCIS (Netherlands)

Lazar, M.

2006-01-01

This thesis considers the stabilization and the robust stabilization of certain classes of hybrid systems using model predictive control. Hybrid systems represent a broad class of dynamical systems in which discrete behavior (usually described by a finite state machine) and continuous behavior

Nuclear Hybrid Energy System Modeling: RELAP5 Dynamic Coupling Capabilities

Energy Technology Data Exchange (ETDEWEB)

Piyush Sabharwall; Nolan Anderson; Haihua Zhao; Shannon Bragg-Sitton; George Mesina

2012-09-01

The nuclear hybrid energy systems (NHES) research team is currently developing a dynamic simulation of an integrated hybrid energy system. A detailed simulation of proposed NHES architectures will allow initial computational demonstration of a tightly coupled NHES to identify key reactor subsystem requirements, identify candidate reactor technologies for a hybrid system, and identify key challenges to operation of the coupled system. This work will provide a baseline for later coupling of design-specific reactor models through industry collaboration. The modeling capability addressed in this report focuses on the reactor subsystem simulation.

A global hybrid coupled model based on atmosphere-SST feedbacks

Energy Technology Data Exchange (ETDEWEB)

Cimatoribus, Andrea A.; Drijfhout, Sybren S. [Royal Netherlands Meteorological Institute, De Bilt (Netherlands); Dijkstra, Henk A. [Utrecht University, Institute for Marine and Atmospheric Research Utrecht, Utrecht (Netherlands)

2012-02-15

A global hybrid coupled model is developed, with the aim of studying the effects of ocean-atmosphere feedbacks on the stability of the Atlantic meridional overturning circulation. The model includes a global ocean general circulation model and a statistical atmosphere model. The statistical atmosphere model is based on linear regressions of data from a fully coupled climate model on sea surface temperature both locally and hemispherically averaged, being the footprint of Atlantic meridional overturning variability. It provides dynamic boundary conditions to the ocean model for heat, freshwater and wind-stress. A basic but consistent representation of ocean-atmosphere feedbacks is captured in the hybrid coupled model and it is more than 10 times faster than the fully coupled climate model. The hybrid coupled model reaches a steady state with a climate close to the one of the fully coupled climate model, and the two models also have a similar response (collapse) of the Atlantic meridional overturning circulation to a freshwater hosing applied in the northern North Atlantic. (orig.)

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

Science.gov (United States)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

Hybrid feedback feedforward: An efficient design of adaptive neural network control.

Science.gov (United States)

Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

2016-04-01

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.

Craniomandibular form and body size variation of first generation mouse hybrids: A model for hominin hybridization.

Science.gov (United States)

Warren, Kerryn A; Ritzman, Terrence B; Humphreys, Robyn A; Percival, Christopher J; Hallgrímsson, Benedikt; Ackermann, Rebecca Rogers

2018-03-01

Hybridization occurs in a number of mammalian lineages, including among primate taxa. Analyses of ancient genomes have shown that hybridization between our lineage and other archaic hominins in Eurasia occurred numerous times in the past. However, we still have limited empirical data on what a hybrid skeleton looks like, or how to spot patterns of hybridization among fossils for which there are no genetic data. Here we use experimental mouse models to supplement previous studies of primates. We characterize size and shape variation in the cranium and mandible of three wild-derived inbred mouse strains and their first generation (F 1 ) hybrids. The three parent taxa in our analysis represent lineages that diverged over approximately the same period as the human/Neanderthal/Denisovan lineages and their hybrids are variably successful in the wild. Comparisons of body size, as quantified by long bone measurements, are also presented to determine whether the identified phenotypic effects of hybridization are localized to the cranium or represent overall body size changes. The results indicate that hybrid cranial and mandibular sizes, as well as limb length, exceed that of the parent taxa in all cases. All three F 1 hybrid crosses display similar patterns of size and form variation. These results are generally consistent with earlier studies on primates and other mammals, suggesting that the effects of hybridization may be similar across very different scenarios of hybridization, including different levels of hybrid fitness. This paper serves to supplement previous studies aimed at identifying F 1 hybrids in the fossil record and to introduce further research that will explore hybrid morphologies using mice as a proxy for better understanding hybridization in the hominin fossil record. Copyright © 2017 Elsevier Ltd. All rights reserved.

A model for particle acceleration in lower hybrid collapse

International Nuclear Information System (INIS)

Retterer, J.M.

1997-01-01

A model for particle acceleration during the nonlinear collapse of lower hybrid waves is described. Using the Musher-Sturman wave equation to describe the effects of nonlinear processes and a velocity diffusion equation for the particle velocity distribution, the model self-consistently describes the exchange of energy between the fields and the particles in the local plasma. Two-dimensional solutions are presented for the modulational instability of a plane wave and the collapse of a cylindrical wave packet. These calculations were motivated by sounding rocket observations in the vicinity of auroral arcs in the Earth close-quote s ionosphere, which have revealed the existence of large-amplitude lower-hybrid wave packets associated with ions accelerated to energies of 100 eV. The scaling of the sizes of these wave packets is consistent with the theory of lower-hybrid collapse and the observed lower-hybrid field amplitudes are adequate to accelerate the ionospheric ions to the observed energies

Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor

Directory of Open Access Journals (Sweden)

Amos O. Anele

2018-04-01

Full Text Available In a previous paper, a number of potential models for short-term water demand (STWD prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017 showed that hybrid models may be considered as the accurate and appropriate forecasting models for STWD prediction. However, such best single valued forecast does not guarantee reliable and robust decisions, which can be properly obtained via model uncertainty processors (MUPs. MUPs provide an estimate of the full predictive densities and not only the single valued expected prediction. Amongst other MUPs, the purpose of this paper is to use the multi-variate version of the model conditional processor (MCP, proposed by Todini (2008, to demonstrate how the estimation of the predictive probability conditional to a number of relatively good predictive models may improve our knowledge, thus reducing the predictive uncertainty (PU when forecasting into the unknown future. Through the MCP approach, the probability distribution of the future water demand can be assessed depending on the forecast provided by one or more deterministic forecasting models. Based on an average weekly data of 168 h, the probability density of the future demand is built conditional on three models’ predictions, namely the autoregressive-moving average (ARMA, feed-forward back propagation neural network (FFBP-NN and hybrid model (i.e., combined forecast from ARMA and FFBP-NN. The results obtained show that MCP may be effectively used for real-time STWD prediction since it brings out the PU connected to its forecast, and such information could help water utilities estimate the risk connected to a decision.

PREFACE: 11th International Conference on Nucleus-Nucleus Collisions (NN2012)

Science.gov (United States)

Li, Bao-An; Natowitz, Joseph B.

2013-03-01

The 11th International Conference on Nucleus-Nucleus Collisions (NN2012) was held from 27 May to 1 June 2012, in San Antonio, Texas, USA. It was jointly organized and hosted by The Cyclotron Institute at Texas A&M University, College Station and The Department of Physics and Astronomy at Texas A&M University-Commerce. Among the approximately 300 participants were a large number of graduate students and post-doctoral fellows. The Keynote Talk of the conference, 'The State of Affairs of Present and Future Nucleus-Nucleus Collision Science', was given by Dr Robert Tribble, University Distinguished Professor and Director of the TAMU Cyclotron Institute. During the conference a very well-received public lecture on neutrino astronomy, 'The ICEcube project', was given by Dr Francis Halzen, Hilldale and Gregory Breit Distinguished Professor at the University of Wisconsin, Madison. The Scientific program continued in the general spirit and intention of this conference series. As is typical of this conference a broad range of topics including fundamental areas of nuclear dynamics, structure, and applications were addressed in 42 plenary session talks, 150 parallel session talks, and 21 posters. The high quality of the work presented emphasized the vitality and relevance of the subject matter of this conference. Following the tradition, the NN2012 International Advisory Committee selected the host and site of the next conference in this series. The 12th International Conference on Nucleus-Nucleus Collisions (NN2015) will be held 21-26 June 2015 in Catania, Italy. It will be hosted by The INFN, Laboratori Nazionali del Sud, INFN, Catania and the Dipartimento di Fisica e Astronomia of the University of Catania. The NN2012 Proceedings contains the conference program and 165 articles organized into the following 10 sections 1. Heavy and Superheavy Elements 2. QCD and Hadron Physics 3. Relativistic Heavy-Ion Collisions 4. Nuclear Structure 5. Nuclear Energy and Applications of

Hybrid Modeling and Optimization of Yogurt Starter Culture Continuous Fermentation

Directory of Open Access Journals (Sweden)

Silviya Popova

2009-10-01

Full Text Available The present paper presents a hybrid model of yogurt starter mixed culture fermentation. The main nonlinearities within a classical structure of continuous process model are replaced by neural networks. The new hybrid model accounts for the dependence of the two microorganisms' kinetics from the on-line measured characteristics of the culture medium - pH. Then the model was used further for calculation of the optimal time profile of pH. The obtained results are with agreement with the experimental once.

Transient Model of Hybrid Concentrated Photovoltaic with Thermoelectric Generator

DEFF Research Database (Denmark)

Mahmoudi Nezhad, Sajjad; Qing, Shaowei; Rezaniakolaei, Alireza

2017-01-01

Transient performance of a concentrated photovoltaic thermoelectric (CPV-TEG) hybrid system is modeled and investigated. A heat sink with water, as the working fluid has been implemented as the cold reservoir of the hybrid system to harvest the heat loss from CPV cell and to increase the efficiency...

Northern Edge Navajo Casino, Fruitland, NM: NN0030343

Science.gov (United States)

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.

Bounded Model Checking and Inductive Verification of Hybrid Discrete-Continuous Systems

DEFF Research Database (Denmark)

Becker, Bernd; Behle, Markus; Eisenbrand, Fritz

2004-01-01

We present a concept to signicantly advance the state of the art for bounded model checking (BMC) and inductive verication (IV) of hybrid discrete-continuous systems. Our approach combines the expertise of partners coming from dierent domains, like hybrid systems modeling and digital circuit veri...

Relativistic three-body approach to NN scattering at intermediate energies

International Nuclear Information System (INIS)

van Faassen, E.; Tjon, J.A.

1986-01-01

The Bethe-Salpeter equation for coupled-channel N-Δ scattering is extended to satisfy unitarity in the NN and NNπ sectors. The procedure eliminates the unitarity violations characteristic of the standard ladder Bethe-Salpeter equation in the inelastic region, and improves the description of pion production near threshold. Results are presented for the NN phase shift and a number of observables up to 1 GeV. In particular, the 1D 2 inelasticity is found to be considerably smaller than found from phase shift analysis. In this context, the importance of the pion deuteron channel for the inelasticity parameter of is pointed out. 33 refs., 16 figs., 4 tabs

Modeling of hybrid vehicle fuel economy and fuel engine efficiency

Science.gov (United States)

Wu, Wei

"Near-CV" (i.e., near-conventional vehicle) hybrid vehicles, with an internal combustion engine, and a supplementary storage with low-weight, low-energy but high-power capacity, are analyzed. This design avoids the shortcoming of the "near-EV" and the "dual-mode" hybrid vehicles that need a large energy storage system (in terms of energy capacity and weight). The small storage is used to optimize engine energy management and can provide power when needed. The energy advantage of the "near-CV" design is to reduce reliance on the engine at low power, to enable regenerative braking, and to provide good performance with a small engine. The fuel consumption of internal combustion engines, which might be applied to hybrid vehicles, is analyzed by building simple analytical models that reflect the engines' energy loss characteristics. Both diesel and gasoline engines are modeled. The simple analytical models describe engine fuel consumption at any speed and load point by describing the engine's indicated efficiency and friction. The engine's indicated efficiency and heat loss are described in terms of several easy-to-obtain engine parameters, e.g., compression ratio, displacement, bore and stroke. Engine friction is described in terms of parameters obtained by fitting available fuel measurements on several diesel and spark-ignition engines. The engine models developed are shown to conform closely to experimental fuel consumption and motored friction data. A model of the energy use of "near-CV" hybrid vehicles with different storage mechanism is created, based on simple algebraic description of the components. With powertrain downsizing and hybridization, a "near-CV" hybrid vehicle can obtain a factor of approximately two in overall fuel efficiency (mpg) improvement, without considering reductions in the vehicle load.

Repellent Activity of TRIG (N-N Diethyl Benzamide) against Man-Biting Mosquitoes

OpenAIRE

Msangi, Shandala; Kweka, Eliningaya; Mahande, Aneth

2018-01-01

A study was conducted to assess efficacy of a new repellent brand TRIG (15% N-N Diethyl Benzamide) when compared to DEET (20% N-N Methyl Toluamide). The repellents were tested in laboratory and field. In the laboratory, the repellence was tested on human volunteers, by exposing their repellent-treated arms on starved mosquitoes in cages for 3 minutes at hourly intervals, while counting the landing and probing attempts. Anopheles gambiae and Aedes aegypti mosquitoes were used. Field evaluation...

Modeling and optimization of batteryless hybrid PV (photovoltaic)/Diesel systems for off-grid applications

International Nuclear Information System (INIS)

Tsuanyo, David; Azoumah, Yao; Aussel, Didier; Neveu, Pierre

2015-01-01

This paper presents a new model and optimization procedure for off-grid hybrid PV (photovoltaic)/Diesel systems operating without battery storage. The proposed technico-economic model takes into account the variability of both the solar irradiation and the electrical loads. It allows optimizing the design and the operation of the hybrid systems by searching their lowest LCOE (Levelized Cost of Electricity). Two cases have been investigated: identical Diesel generators and Diesel generators with different sizes, and both are compared to conventional standalone Diesel generator systems. For the same load profile, the optimization results show that the LCOE of the optimized batteryless hybrid solar PV/Diesel (0.289 €/kWh for the hybrid system with identical Diesel generators and 0.284 €/kWh for the hybrid system with different sizes of Diesel generators) is lower than the LCOE obtained with standalone Diesel generators (0.32 €/kWh for the both cases). The obtained results are then confirmed by HOMER (Hybrid Optimization Model for Electric Renewables) software. - Highlights: • A technico-economic model for optimal design and operation management of batteryless hybrid systems is developed. • The model allows optimizing design and operation of hybrid systems by ensuring their lowest LCOE. • The model was validated by HOMER. • Batteryless hybrid system are suitable for off-grid applications

The long-acting GLP-1 derivative NN2211 ameliorates glycemia and increases beta-cell mass in diabetic mice

DEFF Research Database (Denmark)

Rolin, Bidda; Larsen, Marianne O; Gotfredsen, Carsten F

2002-01-01

in food intake, there were no significant differences between NN2211 and vehicle treatment, and body weight was not affected. Histological examination revealed that beta-cell proliferation and mass were not increased significantly in ob/ob mice with NN2211, although there was a strong tendency...... for increased proliferation. In db/db mice, exendin-4 and NN2211 decreased blood glucose compared with vehicle, but NN2211 had a longer duration of action. Food intake was lowered only on day 1 with both compounds, and body weight was unaffected. beta-Cell proliferation rate and mass were significantly...

Comparison of a hybrid model to a global model of atmospheric pressure radio-frequency capacitive discharges

International Nuclear Information System (INIS)

Lazzaroni, C; Lieberman, M A; Lichtenberg, A J; Chabert, P

2012-01-01

A one-dimensional hybrid analytical-numerical global model of atmospheric pressure radio-frequency (rf) driven capacitive discharges, previously developed, is compared with a basic global model. A helium feed gas with small admixtures of oxygen is studied. For the hybrid model, the electrical characteristics are calculated analytically as a current-driven homogeneous discharge. The electron power balance is solved analytically to determine a time-varying Maxwellian electron temperature, which oscillates on the rf timescale. Averaging over the rf period yields effective rate coefficients for gas phase activated processes. For the basic global model, the electron temperature is constant in time and the sheath physics is neglected. For both models, the particle balance relations for all species are integrated numerically to determine the equilibrium discharge parameters. Variations of discharge parameters with composition and rf power are determined and compared. The rate coefficients for electron-activated processes are strongly temperature dependent, leading to significantly larger neutral and charged particle densities for the hybrid model. For small devices, finite sheath widths limit the operating regimes to low O 2 fractions. This is captured by the hybrid model but cannot be predicted from the basic global model.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

Science.gov (United States)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

A hybrid agent-based approach for modeling microbiological systems.

Science.gov (United States)

Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

2008-11-21

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.

Mechanisms Underlying Mammalian Hybrid Sterility in Two Feline Interspecies Models.

Science.gov (United States)

Davis, Brian W; Seabury, Christopher M; Brashear, Wesley A; Li, Gang; Roelke-Parker, Melody; Murphy, William J

2015-10-01

The phenomenon of male sterility in interspecies hybrids has been observed for over a century, however, few genes influencing this recurrent phenotype have been identified. Genetic investigations have been primarily limited to a small number of model organisms, thus limiting our understanding of the underlying molecular basis of this well-documented "rule of speciation." We utilized two interspecies hybrid cat breeds in a genome-wide association study employing the Illumina 63 K single-nucleotide polymorphism array. Collectively, we identified eight autosomal genes/gene regions underlying associations with hybrid male sterility (HMS) involved in the function of the blood-testis barrier, gamete structural development, and transcriptional regulation. We also identified several candidate hybrid sterility regions on the X chromosome, with most residing in close proximity to complex duplicated regions. Differential gene expression analyses revealed significant chromosome-wide upregulation of X chromosome transcripts in testes of sterile hybrids, which were enriched for genes involved in chromatin regulation of gene expression. Our expression results parallel those reported in Mus hybrids, supporting the "Large X-Effect" in mammalian HMS and the potential epigenetic basis for this phenomenon. These results support the value of the interspecies feline model as a powerful tool for comparison to rodent models of HMS, demonstrating unique aspects and potential commonalities that underpin mammalian reproductive isolation. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Control-relevant modeling and simulation of a SOFC-GT hybrid system

OpenAIRE

Rambabu Kandepu; Lars Imsland; Christoph Stiller; Bjarne A. Foss; Vinay Kariwala

2006-01-01

In this paper, control-relevant models of the most important components in a SOFC-GT hybrid system are described. Dynamic simulations are performed on the overall hybrid system. The model is used to develop a simple control structure, but the simulations show that more elaborate control is needed.

Modelling and Verifying Communication Failure of Hybrid Systems in HCSP

DEFF Research Database (Denmark)

Wang, Shuling; Nielson, Flemming; Nielson, Hanne Riis

2016-01-01

Hybrid systems are dynamic systems with interacting discrete computation and continuous physical processes. They have become ubiquitous in our daily life, e.g. automotive, aerospace and medical systems, and in particular, many of them are safety-critical. For a safety-critical hybrid system......, in the presence of communication failure, the expected control from the controller will get lost and as a consequence the physical process cannot behave as expected. In this paper, we mainly consider the communication failure caused by the non-engagement of one party in communication action, i.......e. the communication itself fails to occur. To address this issue, this paper proposes a formal framework by extending HCSP, a formal modeling language for hybrid systems, for modeling and verifying hybrid systems in the absence of receiving messages due to communication failure. We present two inference systems...

Resonances in the ΣNN system

International Nuclear Information System (INIS)

Gibson, B.F.; Afnan, I.R.

1993-01-01

We first review certain unique aspects of few-body Α- hypernuclei and then explore the physics of summation threshold in few-body elastic scattering and reactions. In particular, we discuss a predicted enhancement in the Αd cross section near the summation NN threshold in terms of poles in the Τ=0YNN amplitude. A brief discussion of anticipated poles in the Τ=1 amplitudes is also given

Isobar contributions to the NN interaction

International Nuclear Information System (INIS)

Bagnoud, X.; Holinde, K.; Machleidt, R.

1981-01-01

The fourth-order noniterative 2π-exchange diagrams involving double-isobar intermediate states are evaluated in momentum space, in the framework of noncovariant perturbation theory. The role of these diagrams is studied by making a detailed comparison with (i) the corresponding iterative diagrams and (ii) diagrams involving NN or NΔ intermediate states. Current prescriptions for replacing all time-orderings of those diagrams by (simple) pion-range transition potentials are tested

Old Torreon Navajo Day School, Cuba, NM: NN0030341

Science.gov (United States)

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.

A Comparison between a Minijet Model and a Glasma Flux Tube Model for Central Au-Au Collisions at √(ovr sNN)=200 GeV

International Nuclear Information System (INIS)

Longacre, R.S.

2011-01-01

In this paper we compare two models with central Au-Au collisions at √(ovr s NN )=200 GeV. The first model is a minijet model which assumes that around ∼50 minijets are produced in back-to-back pairs and have an altered fragmentation functions. It is also assumed that the fragments are transparent and escape the collision zone and are detected. The second model is a glasma flux tube model which leads to flux tubes on the surface of a radial expanding fireball driven by interacting flux tubes near the center of the fireball through plasma instabilities. This internal fireball becomes an opaque hydro fluid which pushes the surface flux tubes outward. Around ∼12 surface flux tubes remain and fragment with ∼1/2 the produced particles escaping the collision zone and are detected. Both models can reproduce two particle angular correlations in the different p t1 p t2 bins. We also compare the two models for three additional effects: meson baryon ratios; the long range nearside correlation called the ridge; and the so-called mach cone effect when applied to three particle angular correlations.

A hybrid modeling with data assimilation to evaluate human exposure level

Science.gov (United States)

Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

2015-12-01

Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

Scalability of Sustainable Business Models in Hybrid Organizations

Directory of Open Access Journals (Sweden)

Adam Jabłoński

2016-02-01

Full Text Available The dynamics of change in modern business create new mechanisms for company management to determine their pursuit and the achievement of their high performance. This performance maintained over a long period of time becomes a source of ensuring business continuity by companies. An ontological being enabling the adoption of such assumptions is such a business model that has the ability to generate results in every possible market situation and, moreover, it has the features of permanent adaptability. A feature that describes the adaptability of the business model is its scalability. Being a factor ensuring more work and more efficient work with an increasing number of components, scalability can be applied to the concept of business models as the company’s ability to maintain similar or higher performance through it. Ensuring the company’s performance in the long term helps to build the so-called sustainable business model that often balances the objectives of stakeholders and shareholders, and that is created by the implemented principles of value-based management and corporate social responsibility. This perception of business paves the way for building hybrid organizations that integrate business activities with pro-social ones. The combination of an approach typical of hybrid organizations in designing and implementing sustainable business models pursuant to the scalability criterion seems interesting from the cognitive point of view. Today, hybrid organizations are great spaces for building effective and efficient mechanisms for dialogue between business and society. This requires the appropriate business model. The purpose of the paper is to present the conceptualization and operationalization of scalability of sustainable business models that determine the performance of a hybrid organization in the network environment. The paper presents the original concept of applying scalability in sustainable business models with detailed

Construction of six-quark states from parity eigenfunctions for n-n processes

International Nuclear Information System (INIS)

Stancu, F.; Wilets, L.

1987-01-01

The work presented is to classify and construct six-quark states as totally antisymmetric states of six fermions, each described by orbital, spin, isospin, and color degrees of freedom. A classification scheme is proposed based on parity eigenfunctions. The single-particle hamiltonian is assumed to be reflectionally and axially symmetric and can be obtained, for example, from constrained Hartree-Fock or solition mean field theories. The ultimate aim is to study N-N processes in the context of the (relativistic) soliton bag model

A hybrid parallel framework for the cellular Potts model simulations

Energy Technology Data Exchange (ETDEWEB)

Jiang, Yi [Los Alamos National Laboratory; He, Kejing [SOUTH CHINA UNIV; Dong, Shoubin [SOUTH CHINA UNIV

2009-01-01

The Cellular Potts Model (CPM) has been widely used for biological simulations. However, most current implementations are either sequential or approximated, which can't be used for large scale complex 3D simulation. In this paper we present a hybrid parallel framework for CPM simulations. The time-consuming POE solving, cell division, and cell reaction operation are distributed to clusters using the Message Passing Interface (MPI). The Monte Carlo lattice update is parallelized on shared-memory SMP system using OpenMP. Because the Monte Carlo lattice update is much faster than the POE solving and SMP systems are more and more common, this hybrid approach achieves good performance and high accuracy at the same time. Based on the parallel Cellular Potts Model, we studied the avascular tumor growth using a multiscale model. The application and performance analysis show that the hybrid parallel framework is quite efficient. The hybrid parallel CPM can be used for the large scale simulation ({approx}10{sup 8} sites) of complex collective behavior of numerous cells ({approx}10{sup 6}).

Control-relevant modeling and simulation of a SOFC-GT hybrid system

Directory of Open Access Journals (Sweden)

Rambabu Kandepu

2006-07-01

Full Text Available In this paper, control-relevant models of the most important components in a SOFC-GT hybrid system are described. Dynamic simulations are performed on the overall hybrid system. The model is used to develop a simple control structure, but the simulations show that more elaborate control is needed.

A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment

International Nuclear Information System (INIS)

Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir

2015-01-01

This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL ® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0–238 N s m −1 through the viscous and electromagnetic components, respectively. (paper)

Narrow coherent effects in πNN-dynamics

International Nuclear Information System (INIS)

Kudryavtsev, A.E.; Obrant, G.Z.

1990-01-01

Coherent effect production is considered in πNN-dynamics with resonant pion-nucleon interaction via Brueckner theory and Faddev equations. It is shown that the narrow energy and final momentum dependence can arise in the inelastic S-wave πd-scattering. The energy dependence peculiarities can have a width an order magnitude less than πN-resonance one

Modeling and optimization of kerf taper and surface roughness in laser cutting of titanium alloy sheet

Energy Technology Data Exchange (ETDEWEB)

Pandey, Arun Kumar; Dubey, Avanish Kumar [Motilal Nehru National Institute of Technology Allahabad, Uttar Pradesh (India)

2013-07-15

Laser cutting of titanium and its alloys is difficult due to it's poor thermal conductivity and chemical reactivity at elevated temperatures. But demand of these materials in different advanced industries such as aircraft, automobile and space research, require accurate geometry with high surface quality. The present research investigates the laser cutting process behavior of titanium alloy sheet (Ti-6Al-4V) with the aim to improve geometrical accuracy and surface quality by minimizing the kerf taper and surface roughness. The data obtained from L{sub 27} orthogonal array experiments have been used for developing neural network (NN) based models of kerf taper and surface roughness. A hybrid approach of neural network and genetic algorithm has been proposed and applied for the optimization of different quality characteristics. The optimization results show considerable improvements in both the quality characteristics. The results predicted by NN models are well in agreement with the experimental data.

Three hybridization models based on local search scheme for job shop scheduling problem

Science.gov (United States)

Balbi Fraga, Tatiana

2015-05-01

This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.

Dynamic Model of Islamic Hybrid Securities: Empirical Evidence From Malaysia Islamic Capital Market

Directory of Open Access Journals (Sweden)

Jaafar Pyeman

2016-12-01

Full Text Available Capital structure selection is fundamentally important in corporate financial management as it influence on mutually return and risk to stakeholders. Despite of Malaysia’s position as one of the major players of Islamic Financial Market, there are still lack of studies has been conducted on the capital structure of shariah compliant firms especially related to hybrid securities. The objective of this study is to determine the hybrid securities issuance model among the shariah compliant firms in Malaysia. As such, this study is to expand the literature review by providing comprehensive analysis on the hybrid capital structure and to develop dynamic Islamic hybrid securities model for shariah compliant firms. We use panel data of 50 companies that have been issuing the hybrid securities from the year of 2004- 2012. The outcomes of the studies are based on the dynamic model GMM estimation for the determinants of hybrid securities. Based on our model, risk and growth are considered as the most determinant factors for issuing convertible bond and loan stock. These results suggest that, the firms that have high risk but having good growth prospect will choose hybrid securities of convertible bond. The model also support the backdoor equity listing hypothesis by Stein (1992 where the hybrid securities enable the profitable firms to venture into positive NPV project by issuing convertible bond as it offer lower coupon rate as compare to the normal debt rate

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

Science.gov (United States)

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.

An inclusive study of the reaction anti NN → K01 + anything below 1.0 GeV/c

International Nuclear Information System (INIS)

Angelini, C.

1975-01-01

Some preliminary results are presented on an inclusive analysis of anti NN annihilations between 0. and 1.0 GeV/c incident anti p momentum. Inclusive K 0 1 distributions are considered and discussed in the framework of the thermodynamical model. (L.M.K.)

Measurement of electrons from beauty-hadron decays in p-Pb collisions at √sNN=5.02 TeV and Pb-Pb collisions at √sNN=2.76 TeV

NARCIS (Netherlands)

The ALICE collaboration, ALICE collaboration; 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.; Albuquerque, D. S. D.; 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.; Janssen, M M; Andrei, C.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C. D.; 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-Moreno, E.; Beltran, L. G. 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.; 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.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; 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.; Iga Buitron, S. A.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; 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.; Cepila, J.; 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, Sukhee; 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.; Crkovská, J.; 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.; Dasgupta, S. 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. Derradi; 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, O.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; 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.; Estienne, M.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; 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.; De 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.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Garg, P.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; 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.; Gonzalez, A. S.; Gonzalez, V; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; 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.; 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.W.; 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.M.; Khan, Shfaqat A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.-S.; 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.L.; 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, Seema; 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.; Strunz-Lehner, Christine; 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, Alicia; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martínez, Isabel M.; 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, Rajiv; 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, J.-W.; 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.; 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. Y.; 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.; 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.

2017-01-01

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 1NN=5.02 TeV and in 1.3 < pT< 8 GeV/c in the 20% most central Pb-Pb collisions at √sNN=2.76

Modelling of hybrid energy system - Part I: Problem formulation and model development

Energy Technology Data Exchange (ETDEWEB)

Gupta, Ajai; Saini, R.P.; Sharma, M.P. [Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 (India)

2011-02-15

A well designed hybrid energy system can be cost effective, has a high reliability and can improve the quality of life in remote rural areas. The economic constraints can be met, if these systems are fundamentally well designed, use appropriate technology and make use effective dispatch control techniques. The first paper of this tri-series paper, presents the analysis and design of a mixed integer linear mathematical programming model (time series) to determine the optimal operation and cost optimization for a hybrid energy generation system consisting of a photovoltaic array, biomass (fuelwood), biogas, small/micro-hydro, a battery bank and a fossil fuel generator. The optimization is aimed at minimizing the cost function based on demand and potential constraints. Further, mathematical models of all other components of hybrid energy system are also developed. This is the generation mix of the remote rural of India; it may be applied to other rural areas also. (author)

Alkali metal control over N-N cleavage in iron complexes.

Science.gov (United States)

Grubel, Katarzyna; Brennessel, William W; Mercado, Brandon Q; Holland, Patrick L

2014-12-03

Though N2 cleavage on K-promoted Fe surfaces is important in the large-scale Haber-Bosch process, there is still ambiguity about the number of Fe atoms involved during the N-N cleaving step and the interactions responsible for the promoting ability of K. This work explores a molecular Fe system for N2 reduction, particularly focusing on the differences in the results obtained using different alkali metals as reductants (Na, K, Rb, Cs). The products of these reactions feature new types of Fe-N2 and Fe-nitride cores. Surprisingly, adding more equivalents of reductant to the system gives a product in which the N-N bond is not cleaved, indicating that the reducing power is not the most important factor that determines the extent of N2 activation. On the other hand, the results suggest that the size of the alkali metal cation can control the number of Fe atoms that can approach N2, which in turn controls the ability to achieve N2 cleavage. The accumulated results indicate that cleaving the triple N-N bond to nitrides is facilitated by simultaneous approach of least three low-valent Fe atoms to a single molecule of N2.

A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

International Nuclear Information System (INIS)

Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

2014-01-01

Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

Three-quark forces and the role of meson exchanges in weak NN interaction

International Nuclear Information System (INIS)

Grach, I.; Shmatikov, M.

1989-01-01

The contribution of weak three-quark forces involving meson exchanges to the longitudinal analyzing power A L in the low-energy pp-scattering is calculated. The nonrelativistic potential model is used for the desorption of strong quark interactions while their weak coupling is described by the Weinberg-Salam lagrangian. The dominant mechanism of parity violation in the NN system (provided the one-pion exchange is forbidden by selection rules) is the contact interaction of quarks. 17 refs.; 3 figs

Prospects for N-N* Transitions from Lattice QCD

International Nuclear Information System (INIS)

Richards, David

2008-01-01

I describe the status of calculations of N-N* transition form factors in lattice QCD, and the prospects for future calculations. I emphasise the need to reliably delineate the states in the spectrum, and the progress that has been made by the Hadron Spectrum Collaboration in so doing.

A modeling method for hybrid energy behaviors in flexible machining systems

International Nuclear Information System (INIS)

Li, Yufeng; He, Yan; Wang, Yan; Wang, Yulin; Yan, Ping; Lin, Shenlong

2015-01-01

Increasingly environmental and economic pressures have led to great concerns regarding the energy consumption of machining systems. Understanding energy behaviors of flexible machining systems is a prerequisite for improving energy efficiency of these systems. This paper proposes a modeling method to predict energy behaviors in flexible machining systems. The hybrid energy behaviors not only depend on the technical specification related of machine tools and workpieces, but are significantly affected by individual production scenarios. In the method, hybrid energy behaviors are decomposed into Structure-related energy behaviors, State-related energy behaviors, Process-related energy behaviors and Assignment-related energy behaviors. The modeling method for the hybrid energy behaviors is proposed based on Colored Timed Object-oriented Petri Net (CTOPN). The former two types of energy behaviors are modeled by constructing the structure of CTOPN, whist the latter two types of behaviors are simulated by applying colored tokens and associated attributes. Machining on two workpieces in the experimental workshop were undertaken to verify the proposed modeling method. The results showed that the method can provide multi-perspective transparency on energy consumption related to machine tools, workpieces as well as production management, and is particularly suitable for flexible manufacturing system when frequent changes in machining systems are often encountered. - Highlights: • Energy behaviors in flexible machining systems are modeled in this paper. • Hybrid characteristics of energy behaviors are examined from multiple viewpoints. • Flexible modeling method CTOPN is used to predict the hybrid energy behaviors. • This work offers a multi-perspective transparency on energy consumption

Hybrid quantum teleportation: A theoretical model

Energy Technology Data Exchange (ETDEWEB)

Takeda, Shuntaro; Mizuta, Takahiro; Fuwa, Maria; Yoshikawa, Jun-ichi; Yonezawa, Hidehiro; Furusawa, Akira [Department of Applied Physics, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan)

2014-12-04

Hybrid quantum teleportation – continuous-variable teleportation of qubits – is a promising approach for deterministically teleporting photonic qubits. We propose how to implement it with current technology. Our theoretical model shows that faithful qubit transfer can be achieved for this teleportation by choosing an optimal gain for the teleporter’s classical channel.

Hybrid model of steam boiler

International Nuclear Information System (INIS)

Rusinowski, Henryk; Stanek, Wojciech

2010-01-01

In the case of big energy boilers energy efficiency is usually determined with the application of the indirect method. Flue gas losses and unburnt combustible losses have a significant influence on the boiler's efficiency. To estimate these losses the knowledge of the operating parameters influence on the flue gases temperature and the content of combustible particles in the solid combustion products is necessary. A hybrid model of a boiler developed with the application of both analytical modelling and artificial intelligence is described. The analytical part of the model includes the balance equations. The empirical models express the dependence of the flue gas temperature and the mass fraction of the unburnt combustibles in solid combustion products on the operating parameters of a boiler. The empirical models have been worked out by means of neural and regression modelling.

Effects of short range ΔN interaction on observables of the πNN system

International Nuclear Information System (INIS)

Alexandrou, C.; Blankleider, B.

1990-01-01

The inadequacy of standard few-body approaches in describing the πNN system has motivated searches for the responsible missing mechanism. In the case of πd scattering, it has recently been asserted that an additional short range ΔN interaction can account for essentially all the discrepancies between a few-body calculation and experimental data. This conclusion, however, has been based on calculations where a phenomenological ΔN interaction is added only in Born term to background few-body amplitudes. In the present work we investigate the effect of including such a ΔN interaction to all orders within a unitary few-body calculation of the πNN system. Besides testing the validity of adding the ΔN interaction in Born term in πd scattering, our fully coupled approach also enables us to see the influence of the same ΔN interaction on the processes NN→πd and NN→NN. For πd elastic scattering, we find that the higher order ΔN interaction terms can have as much influence on πd observables as the lowest order contribution alone. Moreover, we find that the higher order contributions tend to cancel the effect obtained by adding the ΔN interaction in Born term only. The effect of the same ΔN interaction on NN→πd and NN→NN appears to be as significant as in πd→πd, suggesting that future investigations of the short range ΔN interaction should be done in the context of the fully coupled πNN system

A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

Science.gov (United States)

Oh, Yoo Rhee; Kim, Hong Kook

In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

Science.gov (United States)

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-02-08

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

Modeling and Simulation of Renewable Hybrid Power System using Matlab Simulink Environment

Directory of Open Access Journals (Sweden)

Cristian Dragoş Dumitru

2010-12-01

Full Text Available The paper presents the modeling of a solar-wind-hydroelectric hybrid system in Matlab/Simulink environment. The application is useful for analysis and simulation of a real hybrid solar-wind-hydroelectric system connected to a public grid. Application is built on modular architecture to facilitate easy study of each component module influence. Blocks like wind model, solar model, hydroelectric model, energy conversion and load are implemented and the results of simulation are also presented. As an example, one of the most important studies is the behavior of hybrid system which allows employing renewable and variable in time energy sources while providing a continuous supply. Application represents a useful tool in research activity and also in teaching

Hybrid programming model for implicit PDE simulations on multicore architectures

KAUST Repository

Kaushik, Dinesh; Keyes, David E.; Balay, Satish; Smith, Barry F.

2011-01-01

The complexity of programming modern multicore processor based clusters is rapidly rising, with GPUs adding further demand for fine-grained parallelism. This paper analyzes the performance of the hybrid (MPI+OpenMP) programming model in the context of an implicit unstructured mesh CFD code. At the implementation level, the effects of cache locality, update management, work division, and synchronization frequency are studied. The hybrid model presents interesting algorithmic opportunities as well: the convergence of linear system solver is quicker than the pure MPI case since the parallel preconditioner stays stronger when hybrid model is used. This implies significant savings in the cost of communication and synchronization (explicit and implicit). Even though OpenMP based parallelism is easier to implement (with in a subdomain assigned to one MPI process for simplicity), getting good performance needs attention to data partitioning issues similar to those in the message-passing case. © 2011 Springer-Verlag.

Strategy and gaps for modeling, simulation, and control of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Garcia, Humberto E. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hovsapian, Rob [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kinoshita, Robert [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mesina, George L. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Bragg-Sitton, Shannon M. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Boardman, Richard D. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-04-01

The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

Directory of Open Access Journals (Sweden)

Oliveira Rui

2010-09-01

Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

Viittomakielisten muistiasiantuntijoiden näkemyksiä CERAD-tehtäväsarjan viittomakielisestä käännöksestä

OpenAIRE

Näyrä, Taru

2015-01-01

Suomessa laajasti käytössä olevasta muistitestistä, CERAD-tehtäväsarjasta, tuotettiin vuonna 2010 käännös suomalaiselle viittomakielelle. Käännöksen tuotti Kuurojen Palvelusäätiön Memo-projekti Ra-ha-automaattiyhdistyksen tuella. Projektin jatkona toimiva niin ikään RAY:n tukema Memo-ohjelma tuottaa käännöksestä uutta versiota, jossa pyritään korjaamaan ensimmäisessä käännöksessä havaitut puutteet. Opinnäytetyöni tehtävänä on tukea käännöksen tekemistä kokoamalla tietoa siitä, millaisia näkem...

Centrality dependence of bulk fireball properties in √(sNN)=200 GeV Au-Au collisions

International Nuclear Information System (INIS)

Rafelski, Johann; Letessier, Jean; Torrieri, Giorgio

2005-01-01

We explore the centrality dependence of the properties of the dense hadronic matter created in √(s NN )=200 GeV Au-Au collisions at the Relativistic Heavy Ion Collider. Using the statistical hadronization model, we fit particle yields known for 11 centrality bins. We present the resulting model parameters, rapidity yields of physical quantities, and the physical properties of bulk matter at hadronization as function of centrality. We discuss the production of strangeness and entropy

The πNN coupling from high precision np charge exchange at 162 MeV

International Nuclear Information System (INIS)

Nilsson, J.; Blomgren, J.; Conde, H.; Elmgren, K.; Olsson, N.; Ericson, T.E.O.; Uppsala Univ.; Jonsson, O.; Nilsson, L.; Loiseau, B.; Ringbom, A.

1995-02-01

Differential cross sections for unpolarized neutrons of 162 MeV have been measured to high precision with particular attention to the absolute normalisation. These data can be extrapolated precisely and model-independently to the pion pole and give a πNN coupling constant g 2 =14.6±0.3 or f 2 =0.0808±0.0017. This is higher than recently suggested values. (author) 24 refs.; 3 figs.; 1 tab

Artificial neural network modelling in heavy ion collisions

International Nuclear Information System (INIS)

El-dahshan, E.; Radi, A.; El-Bakry, M.Y.; El Mashad, M.

2008-01-01

The neural network (NN) model and parton two fireball model (PTFM) have been used to study the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28 and S 32 on nuclear emulsion. The trained NN shows a better fitting with experimental data than the PTFM calculations. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The NN simulation results prove a strong presence modeling in heavy ion collisions

Hybrid modelling of soil-structure interaction for embedded structures

International Nuclear Information System (INIS)

Gupta, S.; Penzien, J.

1981-01-01

The basic methods currently being used for the analysis of soil-structure interaction fail to properly model three-dimensional embedded structures with flexible foundations. A hybrid model for the analysis of soil-structure interaction is developed in this investigation which takes advantage of the desirable features of both the finite element and substructure methods and which minimizes their undesirable features. The hybrid model is obtained by partitioning the total soil-structure system into a nearfield and a far-field with a smooth hemispherical interface. The near-field consists of the structure and a finite region of soil immediately surrounding its base. The entire near-field may be modelled in three-dimensional form using the finite element method; thus, taking advantage of its ability to model irregular geometries, and the non-linear soil behavior in the immediate vicinity of the structure. (orig./WL)

Derivation of the parity-violating NN potential

International Nuclear Information System (INIS)

Zenkin, S.

1981-01-01

The parity-violating NN potential due to vector-meson exchange is considered in the framework of current algebra. A method to calculate the effective P-odd NNV vertex, free of uncertainties due to the existence of the Schwinger and seagull terms, is presented. The final result coincides with the result of the factorization approximation and may be considered as a justification of the approximation

Three-body charmful baryonic B decays B-bar→D(D*)NN-bar

International Nuclear Information System (INIS)

Cheng Haiyang; Yang Kweichou

2002-01-01

We study the charmful three-body baryonic B decays B-bar→D ( * ) NN-bar: the color-allowed modes B-bar 0 →D ( * )+ np-bar and the color-suppressed ones B-bar 0 →D ( * )0 pp-bar. While the D* + /D + production ratio is predicted to be of order 3, it is found that D 0 pp-bar has a similar rate as D* 0 pp-bar. It is pointed out that B-bar 0 →D(D*)NN-bar are dominated by the nucleon vector current or by vector meson intermediate states, whereas B-bar 0 →D 0 pp-bar proceeds mainly via the exchange of the axial-vector intermediate state a 1 (1260). The study of the NN-bar invariant mass distribution in general indicates a threshold baryon pair production; that is, a recoil charmed meson accompanied by a low mass baryon pair except that the spectrum of D 0 pp-bar has a hump at large pp-bar invariant mass m pp-bar ∼3.0 GeV

A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

Directory of Open Access Journals (Sweden)

P.-Y. Chen

2009-01-01

Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

Medium energy measurements of N-N parameters

International Nuclear Information System (INIS)

Ambrose, D.; Bachman, M.; Coffey, P.; Glass, G.; Jobst, B.; McNaughton, K.H.; Nguyen, C.; Riley, P.J.

1993-01-01

The authors report here progress made during the three year period January 1, 1990, to December 31, 1993, for the Department of Energy Three-Year Grant No. DE-FG05-88ER40446, third year. A major part of the work has been associated with nucleon-nucleon (N-N) research carried out at the Nucleon Physics Laboratory (NPL) at the Los Alamos Meson Physics Facility (LAMPF). During this period they also completed data acquisition and analyses of a TRIUMF experiment, but they have no further plans for experimental work at TRIUMF. Other research has been and will be continued to be carried out at BNL, and involves two rare kaon decay experiments, BNL E791, now completed, and a second generation rare kaon decay experiment, E871, which has just this summer completed an engineering test run. The authors are now also members of a proposed experiment, STAR, (Solenoidal Tracker at RHIC) to be carried out at the Relativistic Heavy Ion Collider facility, RHIC, at BNL. The past three years have been a time of rapid change in the focus of the experimental program. A LAMPF experiment, E1097, in which they spent a large amount of effort during the past three years, was terminated due to funding shortages after they had fabricated the detector, but before data acquisition, and consequently they increased their participation in the rare kaon experiment at BNL, E871. It now appears that there will be no LAMPF N-N program after 1993, so that the research efforts will concentrate on the BNL rare kaon decay measurement, E871, and on STAR. The authors expect that STAR, which requires the fabrication of a large colliding beam detector facility, will use an increasing amount of their research efforts during the next few years. In what follows they describe recent progress on the LAMPF and TRIUMF N-N measurements, on the BNL rare kaon decay work, and on the initial work with the STAR group

Modeling hydraulic regenerative hybrid vehicles using AMESim and Matlab/Simulink

Science.gov (United States)

Lynn, Alfred; Smid, Edzko; Eshraghi, Moji; Caldwell, Niall; Woody, Dan

2005-05-01

This paper presents the overview of the simulation modeling of a hydraulic system with regenerative braking used to improve vehicle emissions and fuel economy. Two simulation software packages were used together to enhance the simulation capability for fuel economy results and development of vehicle and hybrid control strategy. AMESim, a hydraulic simulation software package modeled the complex hydraulic circuit and component hardware and was interlinked with a Matlab/Simulink model of the vehicle, engine and the control strategy required to operate the vehicle and the hydraulic hybrid system through various North American and European drive cycles.

Solving Problem of Graph Isomorphism by Membrane-Quantum Hybrid Model

Directory of Open Access Journals (Sweden)

Artiom Alhazov

2015-10-01

Full Text Available This work presents the application of new parallelization methods based on membrane-quantum hybrid computing to graph isomorphism problem solving. Applied membrane-quantum hybrid computational model was developed by authors. Massive parallelism of unconventional computing is used to implement classic brute force algorithm efficiently. This approach does not suppose any restrictions of considered graphs types. The estimated performance of the model is less then quadratic that makes a very good result for the problem of \\textbf{NP} complexity.

A new cascade NN based method to short-term load forecast in deregulated electricity market

International Nuclear Information System (INIS)

Kouhi, Sajjad; Keynia, Farshid

2013-01-01

Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method

Modelling and analysis of real-time and hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Olivero, A

1994-09-29

This work deals with the modelling and analysis of real-time and hybrid systems. We first present the timed-graphs as model for the real-time systems and we recall the basic notions of the analysis of real-time systems. We describe the temporal properties on the timed-graphs using TCTL formulas. We consider two methods for property verification: in one hand we study the symbolic model-checking (based on backward analysis) and in the other hand we propose a verification method derived of the construction of the simulation graph (based on forward analysis). Both methods have been implemented within the KRONOS verification tool. Their application for the automatic verification on several real-time systems confirms the practical interest of our approach. In a second part we study the hybrid systems, systems combining discrete components with continuous ones. As in the general case the analysis of this king of systems is not decidable, we identify two sub-classes of hybrid systems and we give a construction based method for the generation of a timed-graph from an element into the sub-classes. We prove that in one case the timed-graph obtained is bi-similar with the considered system and that there exists a simulation in the other case. These relationships allow the application of the described technics on the hybrid systems into the defined sub-classes. (authors). 60 refs., 43 figs., 8 tabs., 2 annexes.

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

DEFF Research Database (Denmark)

Nielsen, Morten; Lund, Ole

2009-01-01

this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

Directory of Open Access Journals (Sweden)

Araceli Sanchis

2013-04-01

Full Text Available Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network and SVM (Support Vector Machines, within the framework of HMM (Hidden Markov Model in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0:05, proving that the hybrid approach is better suited for the addressed domain.

Synthesis of a hybrid model of the VSC FACTS devices and HVDC technologies

Science.gov (United States)

Borovikov, Yu S.; Gusev, A. S.; Sulaymanov, A. O.; Ufa, R. A.

2014-10-01

The motivation of the presented research is based on the need for development of new methods and tools for adequate simulation of FACTS devices and HVDC systems as part of real electric power systems (EPS). The Research object: An alternative hybrid approach for synthesizing VSC-FACTS and -HVDC hybrid model is proposed. The results: the VSC- FACTS and -HVDC hybrid model is designed in accordance with the presented concepts of hybrid simulation. The developed model allows us to carry out adequate simulation in real time of all the processes in HVDC, FACTS devices and EPS as a whole without any decomposition and limitation on their duration, and also use the developed tool for effective solution of a design, operational and research tasks of EPS containing such devices.

Synthesis of a hybrid model of the VSC FACTS devices and HVDC technologies

International Nuclear Information System (INIS)

Borovikov, Yu S; Gusev, A S; Sulaymanov, A O; Ufa, R A

2014-01-01

The motivation of the presented research is based on the need for development of new methods and tools for adequate simulation of FACTS devices and HVDC systems as part of real electric power systems (EPS). The Research object: An alternative hybrid approach for synthesizing VSC-FACTS and -HVDC hybrid model is proposed. The results: the VSC- FACTS and -HVDC hybrid model is designed in accordance with the presented concepts of hybrid simulation. The developed model allows us to carry out adequate simulation in real time of all the processes in HVDC, FACTS devices and EPS as a whole without any decomposition and limitation on their duration, and also use the developed tool for effective solution of a design, operational and research tasks of EPS containing such devices

Model predictive control of an air suspension system with damping multi-mode switching damper based on hybrid model

Science.gov (United States)

Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long

2017-09-01

This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.

Modelling Chemical Preservation of Plantain Hybrid Fruits

Directory of Open Access Journals (Sweden)

Ogueri Nwaiwu

2017-08-01

Full Text Available New plantain hybrids plants have been developed but not much has been done on the post-harvest keeping quality of the fruits and how they are affected by microbial colonization. Hence fruits from a tetraploid hybrid PITA 2 (TMPx 548-9 obtained by crossing plantain varieties Obino l’Ewai and Calcutta 4 (AA and two local triploid (AAB plantain landraces Agbagba and Obino l’Ewai were subjected to various concentrations of acetic, sorbic and propionic acid to determine the impact of chemical concentration, chemical type and plantain variety on ripening and weight loss of plantain fruits. Analysis of titratable acidity, moisture content and total soluble solids showed that there were no significant differences between fruits of hybrid and local varieties. The longest time to ripening from harvest (24 days was achieved with fruits of Agbagba treated with 3% propionic acid. However, fruits of PITA 2 hybrid treated with propionic and sorbic acid at 3% showed the longest green life which indicated that the chemicals may work better at higher concentrations. The Obino l’Ewai cultivar had the highest weight loss for all chemical types used. Modelling data obtained showed that plantain variety had the most significant effect on ripening and indicates that ripening of the fruits may depend on the plantain variety. It appears that weight loss of fruits from the plantain hybrid and local cultivars was not affected by the plantain variety, chemical type. The chemicals at higher concentrations may have an effect on ripening of the fruits and will need further investigation.

A hybrid hydrostatic and non-hydrostatic numerical model for shallow flow simulations

Science.gov (United States)

Zhang, Jingxin; Liang, Dongfang; Liu, Hua

2018-05-01

Hydrodynamics of geophysical flows in oceanic shelves, estuaries, and rivers, are often studied by solving shallow water model equations. Although hydrostatic models are accurate and cost efficient for many natural flows, there are situations where the hydrostatic assumption is invalid, whereby a fully hydrodynamic model is necessary to increase simulation accuracy. There is a growing concern about the decrease of the computational cost of non-hydrostatic pressure models to improve the range of their applications in large-scale flows with complex geometries. This study describes a hybrid hydrostatic and non-hydrostatic model to increase the efficiency of simulating shallow water flows. The basic numerical model is a three-dimensional hydrostatic model solved by the finite volume method (FVM) applied to unstructured grids. Herein, a second-order total variation diminishing (TVD) scheme is adopted. Using a predictor-corrector method to calculate the non-hydrostatic pressure, we extended the hydrostatic model to a fully hydrodynamic model. By localising the computational domain in the corrector step for non-hydrostatic pressure calculations, a hybrid model was developed. There was no prior special treatment on mode switching, and the developed numerical codes were highly efficient and robust. The hybrid model is applicable to the simulation of shallow flows when non-hydrostatic pressure is predominant only in the local domain. Beyond the non-hydrostatic domain, the hydrostatic model is still accurate. The applicability of the hybrid method was validated using several study cases.

A hybrid society model for simulating residential electricity consumption

International Nuclear Information System (INIS)

Xu, Minjie; Hu, Zhaoguang; Wu, Junyong; Zhou, Yuhui

2008-01-01

In this paper, a hybrid social model of econometric model and social influence model is proposed for evaluating the influence of pricing policy and public education policy on residential habit of electricity using in power resources management. And, a hybrid society simulation platform based on the proposed model, called residential electricity consumption multi-agent systems (RECMAS), is designed for simulating residential electricity consumption by multi-agent system. RECMAS is composed of consumer agent, power supplier agent, and policy maker agent. It provides the policy makers with a useful tool to evaluate power price policies and public education campaigns in different scenarios. According to an influenced diffusion mechanism, RECMAS can simulate the residential electricity demand-supply chain and analyze impacts of the factors on residential electricity consumption. Finally, the proposed method is used to simulate urban residential electricity consumption in China. (author)

A hybrid society model for simulating residential electricity consumption

Energy Technology Data Exchange (ETDEWEB)

Xu, Minjie [School of Electrical Engineering, Beijing Jiaotong University, Beijing (China); State Power Economic Research Institute, Beijing (China); Hu, Zhaoguang [State Power Economic Research Institute, Beijing (China); Wu, Junyong; Zhou, Yuhui [School of Electrical Engineering, Beijing Jiaotong University, Beijing (China)

2008-12-15

In this paper, a hybrid social model of econometric model and social influence model is proposed for evaluating the influence of pricing policy and public education policy on residential habit of electricity using in power resources management. And, a hybrid society simulation platform based on the proposed model, called residential electricity consumption multi-agent systems (RECMAS), is designed for simulating residential electricity consumption by multi-agent system. RECMAS is composed of consumer agent, power supplier agent, and policy maker agent. It provides the policy makers with a useful tool to evaluate power price policies and public education campaigns in different scenarios. According to an influenced diffusion mechanism, RECMAS can simulate the residential electricity demand-supply chain and analyze impacts of the factors on residential electricity consumption. Finally, the proposed method is used to simulate urban residential electricity consumption in China. (author)

Neural networks (NN applied to the commercial properties valuation

Directory of Open Access Journals (Sweden)

J. M. Núñez Tabales

2017-03-01

Full Text Available Several agents, such as buyers and sellers, or local or tax authorities need to estimate the value of properties. There are different approaches to obtain the market price of a dwelling. Many papers have been produced in the academic literature for such purposes, but, these are, almost always, oriented to estimate hedonic prices of residential properties, such as houses or apartments. Here these methodologies are used in the field of estimate market price of commercial premises, using AI techniques. A case study is developed in Cordova —city in the South of Spain—. Neural Networks are an attractive alternative to the traditional hedonic modelling approaches, as they are better adapted to non-linearities of causal relationships and they also produce smaller valuation errors. It is also possible, from the NN model, to obtain implicit prices associated to the main attributes that can explain the variability of the market price of commercial properties.

Investigation of {sup 136} Ba gamma-Transitions in the (n,n` gamma) Reaction

Energy Technology Data Exchange (ETDEWEB)

Sergiwa, S M [University of Garyuonis, Benghazi, (Libyan Arab Jamahiriya); Rateb, G M; Zleetni, S M; dufani, M M; Shermit, A M; Al Hamidi, M M [Tajoura Nuclear Research Center, Tripoli, (Libyan Arab Jamahiriya); El-Ahrash, M S [7th of April University, Zawia, (Libyan Arab Jamahiriya)

1995-10-01

Using the reactor fast neutron beam, angular distribution and linear polarization of gamma-rays emitted from the {sup 136} Ba (n,n` gamma) reaction were measured. From these measurements, a decay scheme of {sup 1}36{sup B}a has been constructed. New spin and parity (J{pi}) assignments as well as resolving ambiguities in previous assignments for some levels were done. In addition, multipole mixing rations ({delta} - values, important for model comparison) have been unambiguously determined for many gamma-transitions. 1 fig., 2 tabs.

Narrowing of the balance function with centrality in Au + Au collisions at √sNN

International Nuclear Information System (INIS)

Adams, J.; Alder, 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.; Calderonde la Barca Sanchez, 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.; Guertin, S.M.; Gushin, E.; Hallman, T.J.; Hardtke, D.; Harris, J.W.; Heinz, M.; 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.; Lamont, M.A.C.; Landgraf, J.M.; Lange, S.; Lansdell, C.P.; Lasiuk, B.; Laue, F.; Lauret, J.; Lebedev, A.; Lednicky, 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.; Long, H.

2003-01-01

The balance function is a new observable based on the principle that charge is locally conserved when particles are pair produced. Balance functions have been measured for charged particle pairs and identified charged pion pairs in Au + Au collisions at √sNN = 130 GeV at the Relativistic Heavy Ion Collider using STAR. Balance functions for peripheral collisions have widths consistent with model predictions based on a superposition of nucleon-nucleon scattering. Widths in central collisions are smaller, consistent with trends predicted by models incorporating late hadronization

In-medium NN interactions and nucleon and meson masses studied with nucleon knockout reactions

CERN Document Server

Noro, T; Akiyoshi, H; Daito, I; Fujimura, H; Hatanaka, K; Ihara, F; Ishikawa, T; Ito, M; Kawabata, M; Kawabata, T; Maeda, Y; Matsuoka, N; Morinobu, S; Nakamura, M; Obayashi, E; Okihana, A; Sagara, K; Sakaguchi, H; Takeda, H; Taki, T; Tamii, A; Tamura, K; Yamazaki, H; Yoshida, H; Yoshimura, M; Yosoi, M

2000-01-01

Spin observables have been measured for (p, 2p) reactions aiming at studying medium effects on NN interactions in nuclear field. Observed strong density-dependent reduction of the analyzing power is consistent with a model calculation where reduction of nucleon and meson masses are taken into account. On the other hand, calculations with g-matrices in the Shroedinger framework does not predict the reduction. The spin-transfer coefficients, which data are not reproduced by the model calculation, are found to be sensitive to reduction rate of each meson mass and have a possibility to test scaling lows in mass reductions.

Simulation of Mercury's magnetosheath with a combined hybrid-paraboloid model

Science.gov (United States)

Parunakian, David; Dyadechkin, Sergey; Alexeev, Igor; Belenkaya, Elena; Khodachenko, Maxim; Kallio, Esa; Alho, Markku

2017-08-01

In this paper we introduce a novel approach for modeling planetary magnetospheres that involves a combination of the hybrid model and the paraboloid magnetosphere model (PMM); we further refer to it as the combined hybrid model. While both of these individual models have been successfully applied in the past, their combination enables us both to overcome the traditional difficulties of hybrid models to develop a self-consistent magnetic field and to compensate the lack of plasma simulation in the PMM. We then use this combined model to simulate Mercury's magnetosphere and investigate the geometry and configuration of Mercury's magnetosheath controlled by various conditions in the interplanetary medium. The developed approach provides a unique comprehensive view of Mercury's magnetospheric environment for the first time. Using this setup, we compare the locations of the bow shock and the magnetopause as determined by simulations with the locations predicted by stand-alone PMM runs and also verify the magnetic and dynamic pressure balance at the magnetopause. We also compare the results produced by these simulations with observational data obtained by the magnetometer on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft along a dusk-dawn orbit and discuss the signatures of the magnetospheric features that appear in these simulations. Overall, our analysis suggests that combining the semiempirical PMM with a self-consistent global kinetic model creates new modeling possibilities which individual models cannot provide on their own.

Matkustussäännön toteuttaminen ja kehittäminen - Case Norilsk Nickel Harjavalta Oy

OpenAIRE

Sihvonen, Aino

2015-01-01

Tämä opinnäytetyö tehtiin toimeksiantona Norilsk Nickel Harjavalta Oy:lle. Opinnäytetyön tarkoituksena oli tutkia toimeksiantajan matkustussäännön vahvuuksia ja puutteita. Tutkimustehtävänä oli selvittää, miten yhtiön henkilöstö kokee nykyisen matkustussäännön sekä mistä johtuu, että matkustusasioissa ilmenee erilaisia käytänteitä. Tutkimuksen tavoitteena oli luoda kehitysehdotuksia yhtiön matkustussääntöön. Matkustussäännön kehittämisellä pyritään kustannustehokkuuden ja ympäristönäkökulmien...

Hybrid Electric Vehicle Experimental Model with CAN Network Real Time Control

Directory of Open Access Journals (Sweden)

RATOI, M.

2010-05-01

Full Text Available In this paper an experimental model with a distributed control system of a hybrid electrical vehicle is presented. A communication CAN network of high speed (1 Mbps assures a distributed control of the all components. The modeling and the control of different operating regimes are realized on an experimental test-bench of a hybrid electrical vehicle. The experimental results concerning the variations of the mains variables (currents, torques, speeds are presented.

Hybrid neural network bushing model for vehicle dynamics simulation

International Nuclear Information System (INIS)

Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

2008-01-01

Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers

Bias-dependent hybrid PKI empirical-neural model of microwave FETs

Science.gov (United States)

Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera

2011-10-01

Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.

Modified GMDH-NN algorithm and its application for global sensitivity analysis

Science.gov (United States)

Song, Shufang; Wang, Lu

2017-11-01

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages.

The crossing phenomenon and power of the 1-NN rule

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel; Krayem, S.

submitted 2017 (2018) ISSN 0176-4268 R&D Projects: GA MŠk(CZ) LG15047 Institutional support: RVO:67985807 Keywords : kNN rule * multivariate data * classification * distance * nearest neighbor * distribution mapping function Impact factor: 3.083, year: 2016

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

Directory of Open Access Journals (Sweden)

René Felix Reinhart

2017-02-01

Full Text Available Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

Science.gov (United States)

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-01-01

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

KAUST Repository

Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim

2014-01-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using

Fluid and hybrid models for streamers

Science.gov (United States)

Bonaventura, Zdeněk

2016-09-01

Streamers are contracted ionizing waves with self-generated field enhancement that propagate into a low-ionized medium exposed to high electric field leaving filamentary trails of plasma behind. The widely used model to study streamer dynamics is based on drift-diffusion equations for electrons and ions, assuming local field approximation, coupled with Poisson's equation. For problems where presence of energetic electrons become important a fluid approach needs to be extended by a particle model, accompanied also with Monte Carlo Collision technique, that takes care of motion of these electrons. A combined fluid-particle approach is used to study an influence of surface emission processes on a fast-pulsed dielectric barrier discharge in air at atmospheric pressure. It is found that fluid-only model predicts substantially faster reignition dynamics compared to coupled fluid-particle model. Furthermore, a hybrid model can be created in which the population of electrons is divided in the energy space into two distinct groups: (1) low energy `bulk' electrons that are treated with fluid model, and (2) high energy `beam' electrons, followed as particles. The hybrid model is then capable not only to deal with streamer discharges in laboratory conditions, but also allows us to study electron acceleration in streamer zone of lighting leaders. There, the production of fast electrons from streamers is investigated, since these (runaway) electrons act as seeds for the relativistic runaway electron avalanche (RREA) mechanism, important for high-energy atmospheric physics phenomena. Results suggest that high energy electrons effect the streamer propagation, namely the velocity, the peak electric field, and thus also the production rate of runaway electrons. This work has been supported by the Czech Science Foundation research project 15-04023S.

Gravitational waves in hybrid quintessential inflationary models

International Nuclear Information System (INIS)

Sa, Paulo M; Henriques, Alfredo B

2011-01-01

The generation of primordial gravitational waves is investigated within the hybrid quintessential inflationary model. Using the method of continuous Bogoliubov coefficients, we calculate the full gravitational-wave energy spectrum. The post-inflationary kination period, characteristic of quintessential inflationary models, leaves a clear signature on the spectrum, namely, a sharp rise of the gravitational-wave spectral energy density Ω GW at high frequencies. For appropriate values of the parameters of the model, Ω GW can be as high as 10 -12 in the MHz-GHz range of frequencies.

Two-field axion-monodromy hybrid inflation model: Dante's Waterfall

Science.gov (United States)

Carone, Christopher D.; Erlich, Joshua; Sensharma, Anuraag; Wang, Zhen

2015-02-01

We describe a hybrid axion-monodromy inflation model motivated by the Dante's Inferno scenario. In Dante's Inferno, a two-field potential features a stable trench along which a linear combination of the two fields slowly rolls, rendering the dynamics essentially identical to that of single-field chaotic inflation. A shift symmetry allows for the Lyth bound to be effectively evaded as in other axion-monodromy models. In our proposal, the potential is concave downward near the origin and the inflaton trajectory is a gradual downward spiral, ending at a point where the trench becomes unstable. There, the fields begin falling rapidly towards the minimum of the potential and inflation terminates as in a hybrid model. We find parameter choices that reproduce observed features of the cosmic microwave background, and discuss our model in light of recent results from the BICEP2 and Planck experiments.

Numerical modeling of hybrid fiber-reinforced concrete (hyfrc)

International Nuclear Information System (INIS)

Hameed, R.; Turatsinze, A.

2015-01-01

A model for numerical simulation of mechanical response of concrete reinforced with slipping and non slipping metallic fibers in hybrid form is presented in this paper. Constitutive law used to model plain concrete behaviour is based on plasticity and damage theories, and is capable to determine localized crack opening in three dimensional (3-D) systems. Behaviour law used for slipping metallic fibers is formulated based on effective stress carried by these fibers after when concrete matrix is cracked. A continuous approach is proposed to model the effect of addition of non-slipping metallic fibers in plain concrete. This approach considers the constitutive law of concrete matrix with increased fracture energy in tension obtained experimentally in direct tension tests on Fiber Reinforced Concrete (FRC). To simulate the mechanical behaviour of hybrid fiber-reinforced concrete (HyFRC), proposed approaches to model non-slipping metallic fibers and constitutive law of plain concrete and slipping fibers are used simultaneously without any additive equation. All the parameters used by the proposed model have physical meanings and are determined through experiments or drawn from literature. The model was implemented in Finite Element (FE) Code CASTEM and tested on FRC prismatic notched specimens in flexure. Model prediction showed good agreement with experimental results. (author)

Design and fabrication of a hybrid maglev model employing PML and SML

Science.gov (United States)

Sun, R. X.; Zheng, J.; Zhan, L. J.; Huang, S. Y.; Li, H. T.; Deng, Z. G.

2017-10-01

A hybrid maglev model combining permanent magnet levitation (PML) and superconducting magnetic levitation (SML) was designed and fabricated to explore a heavy-load levitation system advancing in passive stability and simple structure. In this system, the PML was designed to levitate the load, and the SML was introduced to guarantee the stability. In order to realize different working gaps of the two maglev components, linear bearings were applied to connect the PML layer (for load) and the SML layer (for stability) of the hybrid maglev model. Experimental results indicate that the hybrid maglev model possesses excellent advantages of heavy-load ability and passive stability at the same time. This work presents a possible way to realize a heavy-load passive maglev concept.

Dynamic Modeling and Simulation on a Hybrid Power System for Electric Vehicle Applications

Directory of Open Access Journals (Sweden)

Hong-Wen He

2010-11-01

Full Text Available Hybrid power systems, formed by combining high-energy-density batteries and high-power-density ultracapacitors in appropriate ways, provide high-performance and high-efficiency power systems for electric vehicle applications. This paper first establishes dynamic models for the ultracapacitor, the battery and a passive hybrid power system, and then based on the dynamic models a comparative simulation between a battery only power system and the proposed hybrid power system was done under the UDDS (Urban Dynamometer Driving Schedule. The simulation results showed that the hybrid power system could greatly optimize and improve the efficiency of the batteries and their dynamic current was also decreased due to the participation of the ultracapacitors, which would have a good influence on batteries’ cycle life. Finally, the parameter matching for the passive hybrid power system was studied by simulation and comparisons.

Dynamic Modeling and Simulation of a Switched Reluctance Motor in a Series Hybrid Electric Vehicle

OpenAIRE

Siavash Sadeghi; Mojtaba Mirsalim; Arash Hassanpour Isfahani

2010-01-01

Dynamic behavior analysis of electric motors is required in order to accuratelyevaluate the performance, energy consumption and pollution level of hybrid electricvehicles. Simulation tools for hybrid electric vehicles are divided into steady state anddynamic models. Tools with steady-state models are useful for system-level analysiswhereas tools that utilize dynamic models give in-depth information about the behavior ofsublevel components. For the accurate prediction of hybrid electric vehicl...

Structural hybrid reliability index and its convergent solving method based on random–fuzzy–interval reliability model

Directory of Open Access Journals (Sweden)

Hai An

2016-08-01

Full Text Available Aiming to resolve the problems of a variety of uncertainty variables that coexist in the engineering structure reliability analysis, a new hybrid reliability index to evaluate structural hybrid reliability, based on the random–fuzzy–interval model, is proposed in this article. The convergent solving method is also presented. First, the truncated probability reliability model, the fuzzy random reliability model, and the non-probabilistic interval reliability model are introduced. Then, the new hybrid reliability index definition is presented based on the random–fuzzy–interval model. Furthermore, the calculation flowchart of the hybrid reliability index is presented and it is solved using the modified limit-step length iterative algorithm, which ensures convergence. And the validity of convergent algorithm for the hybrid reliability model is verified through the calculation examples in literature. In the end, a numerical example is demonstrated to show that the hybrid reliability index is applicable for the wear reliability assessment of mechanisms, where truncated random variables, fuzzy random variables, and interval variables coexist. The demonstration also shows the good convergence of the iterative algorithm proposed in this article.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

Science.gov (United States)

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

Performance modeling of hybrid MPI/OpenMP scientific applications on large-scale multicore supercomputers

KAUST Repository

Wu, Xingfu; Taylor, Valerie

2013-01-01

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore supercomputers: IBM POWER4, POWER5+ and BlueGene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks and Intel's MPI benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore supercomputers because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyrokinetic Toroidal Code (GTC) in magnetic fusion to validate our performance model of the hybrid application on these multicore supercomputers. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore supercomputers. © 2013 Elsevier Inc.

Performance modeling of hybrid MPI/OpenMP scientific applications on large-scale multicore supercomputers

KAUST Repository

Wu, Xingfu

2013-12-01

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore supercomputers: IBM POWER4, POWER5+ and BlueGene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks and Intel\\'s MPI benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore supercomputers because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyrokinetic Toroidal Code (GTC) in magnetic fusion to validate our performance model of the hybrid application on these multicore supercomputers. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore supercomputers. © 2013 Elsevier Inc.

AMITIS: A 3D GPU-Based Hybrid-PIC Model for Space and Plasma Physics

Science.gov (United States)

Fatemi, Shahab; Poppe, Andrew R.; Delory, Gregory T.; Farrell, William M.

2017-05-01

We have developed, for the first time, an advanced modeling infrastructure in space simulations (AMITIS) with an embedded three-dimensional self-consistent grid-based hybrid model of plasma (kinetic ions and fluid electrons) that runs entirely on graphics processing units (GPUs). The model uses NVIDIA GPUs and their associated parallel computing platform, CUDA, developed for general purpose processing on GPUs. The model uses a single CPU-GPU pair, where the CPU transfers data between the system and GPU memory, executes CUDA kernels, and writes simulation outputs on the disk. All computations, including moving particles, calculating macroscopic properties of particles on a grid, and solving hybrid model equations are processed on a single GPU. We explain various computing kernels within AMITIS and compare their performance with an already existing well-tested hybrid model of plasma that runs in parallel using multi-CPU platforms. We show that AMITIS runs ∼10 times faster than the parallel CPU-based hybrid model. We also introduce an implicit solver for computation of Faraday’s Equation, resulting in an explicit-implicit scheme for the hybrid model equation. We show that the proposed scheme is stable and accurate. We examine the AMITIS energy conservation and show that the energy is conserved with an error < 0.2% after 500,000 timesteps, even when a very low number of particles per cell is used.

Gravitational waves in hybrid quintessential inflationary models

Energy Technology Data Exchange (ETDEWEB)

Sa, Paulo M [Departamento de Fisica, Faculdade de Ciencias e Tecnologia, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro (Portugal); Henriques, Alfredo B, E-mail: pmsa@ualg.pt, E-mail: alfredo.henriques@ist.utl.pt [Centro Multidisciplinar de Astrofisica - CENTRA and Departamento de Fisica, Instituto Superior Tecnico, UTL, Av. Rovisco Pais, 1049-001 Lisboa (Portugal)

2011-09-22

The generation of primordial gravitational waves is investigated within the hybrid quintessential inflationary model. Using the method of continuous Bogoliubov coefficients, we calculate the full gravitational-wave energy spectrum. The post-inflationary kination period, characteristic of quintessential inflationary models, leaves a clear signature on the spectrum, namely, a sharp rise of the gravitational-wave spectral energy density {Omega}{sub GW} at high frequencies. For appropriate values of the parameters of the model, {Omega}{sub GW} can be as high as 10{sup -12} in the MHz-GHz range of frequencies.

Transverse momentum spectra and nuclear modification factors of charged particles in Xe–Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV

CERN Document Server

Acharya, Shreyasi; The ALICE collaboration; Adamova, Dagmar; Adolfsson, Jonatan; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Al-turany, Mohammad; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Ali, Yasir; Alici, Andrea; Alkin, Anton; Alme, Johan; Alt, Torsten; Altenkamper, Lucas; Altsybeev, Igor; Andrei, Cristian; Andreou, Dimitra; Andrews, Harry Arthur; Andronic, Anton; Angeletti, Massimo; Anguelov, Venelin; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Anwar, Rafay; Apadula, Nicole; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Baldisseri, Alberto; Ball, Markus; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barioglio, Luca; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartsch, Esther; Bastid, Nicole; Basu, Sumit; Batigne, Guillaume; Batyunya, Boris; Batzing, Paul Christoph; Bazo Alba, Jose Luis; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Espinoza Beltran, Lucina Gabriela; Belyaev, Vladimir; Bencedi, Gyula; Beole, Stefania; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhaduri, Partha Pratim; Bhasin, Anju; Bhat, Inayat Rasool; Bhatt, Himani; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Antonio; Bianchi, Livio; Bianchi, Nicola; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Boca, Gianluigi; Bock, Friederike; Bogdanov, Alexey; Boldizsar, Laszlo; Bombara, Marek; Bonomi, Germano; Bonora, Matthias; Borel, Herve; Borissov, Alexander; Borri, Marcello; Botta, Elena; Bourjau, Christian; Bratrud, Lars; Braun-munzinger, Peter; Bregant, Marco; Broker, Theo Alexander; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buhler, Paul; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Caines, Helen Louise; Caliva, Alberto; Calvo Villar, Ernesto; Soto Camacho, Rabi; Camerini, Paolo; Capon, Aaron Allan; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Chandra, Sinjini; Chang, Beomsu; Chang, Wan; Chapeland, Sylvain; Chartier, Marielle; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Chowdhury, Tasnuva; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Concas, Matteo; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Costanza, Susanna; Crkovska, Jana; Crochet, Philippe; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Derradi De Souza, Rafael; Franz Degenhardt, Hermann; Deisting, Alexander; Deloff, Andrzej; Delsanto, Silvia; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Ruzza, Benedetto; Arteche Diaz, Raul; Dietel, Thomas; Dillenseger, Pascal; Ding, Yanchun; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Van Doremalen, Lennart Vincent; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dudi, Sandeep; Duggal, Ashpreet Kaur; Dukhishyam, Mallick; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erhardt, Filip; Ersdal, Magnus Rentsch; Espagnon, Bruno; Eulisse, Giulio; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Fabbietti, Laura; Faggin, Mattia; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Fernandez Tellez, Arturo; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiorenza, Gabriele; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Francisco, Audrey; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gajdosova, Katarina; Gallio, Mauro; Duarte Galvan, Carlos; Ganoti, Paraskevi; Garabatos Cuadrado, Jose; Garcia-solis, Edmundo Javier; Garg, Kunal; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; De Leone Gay, Maria Beatriz; Germain, Marie; Ghosh, Jhuma; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Greiner, Leo Clifford; Grelli, Alessandro; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Gronefeld, Julius Maximilian; Grosa, Fabrizio; Grosse-oetringhaus, Jan Fiete; Grosso, Raffaele; Guernane, Rachid; Guerzoni, Barbara; Guittiere, Manuel; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Bautista Guzman, Irais; Haake, Rudiger; Habib, Michael Karim; Hadjidakis, Cynthia Marie; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Hannigan, Ryan; Haque, Md Rihan; Harris, John William; Harton, Austin Vincent; Hassan, Hadi; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Gonzalez Hernandez, Emma; Herrera Corral, Gerardo Antonio; Herrmann, Florian; Hetland, Kristin Fanebust; Hilden, Timo Eero; Hillemanns, Hartmut; Hills, Christopher; Hippolyte, Boris; Hohlweger, Bernhard; Horak, David; Hornung, Sebastian; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Huang, Chun-lu; Hughes, Charles; Huhn, Patrick; Humanic, Thomas; Hushnud, Hushnud; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Iddon, James Philip; Iga Buitron, Sergio Arturo; Ilkaev, Radiy; Inaba, Motoi; Ippolitov, Mikhail; Islam, Md Samsul; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacak, Barbara; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jaelani, Syaefudin; Jahnke, Cristiane; Jakubowska, Monika Joanna; Janik, Malgorzata Anna; Jena, Chitrasen; Jercic, Marko; Jimenez Bustamante, Raul Tonatiuh; Jin, Muqing; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karczmarczyk, Przemyslaw; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Ketzer, Bernhard Franz; Khabanova, Zhanna; Khan, Shaista; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Khatun, Anisa; Khuntia, Arvind; Kielbowicz, Miroslaw Marek; Kileng, Bjarte; Kim, Byungchul; Kim, Daehyeok; Kim, Dong Jo; Kim, Eun Joo; Kim, Hyeonjoong; Kim, Jinsook; Kim, Jiyoung; Kim, Minjung; Kim, Se Yong; Kim, Taejun; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Varga-kofarago, Monika; Kohler, Markus Konrad; Kollegger, Thorsten; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Konyushikhin, Maxim; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Kralik, Ivan; Kravcakova, Adela; Kreis, Lukas; Krivda, Marian; Krizek, Filip; Kruger, Mario; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kundu, Sourav; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kushpil, Svetlana; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Lai, Yue Shi; Lakomov, Igor; Langoy, Rune; Lapidus, Kirill; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Larionov, Pavel; Laudi, Elisa; Lavicka, Roman; Lea, Ramona; Leardini, Lucia; Lee, Seongjoo; Lehas, Fatiha; Lehner, Sebastian; Lehrbach, Johannes; Lemmon, Roy Crawford; Leogrande, Emilia; Leon Monzon, Ildefonso; Levai, Peter; Li, Xiaomei; Li, Xing Long; Lien, Jorgen Andre; Lietava, Roman; Lim, Bong-hwi; Lindal, Svein; Lindenstruth, Volker; Lindsay, Scott William; Lippmann, Christian; Lisa, Michael Annan; Litichevskyi, Vladyslav; Liu, Alwina; Ljunggren, Hans Martin; Llope, William; Lodato, Davide Francesco; Loginov, Vitaly; Loizides, Constantinos; Loncar, Petra; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Luhder, Jens Robert; Lunardon, Marcello; Luparello, Grazia; Lupi, Matteo; Maevskaya, Alla; Mager, Magnus; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Malik, Qasim Waheed; Malinina, Liudmila; Mal'kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-garcia, Gines; Martinez Pedreira, Miguel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Masson, Erwann; Mastroserio, Annalisa; Mathis, Andreas Michael; Toledo Matuoka, Paula Fernanda; Matyja, Adam Tomasz; Mayer, Christoph; Mazzilli, Marianna; Mazzoni, Alessandra Maria; Meddi, Franco; Melikyan, Yuri; Menchaca-rocha, Arturo Alejandro; Meninno, Elisa; Mercado-perez, Jorge; Meres, Michal; Soncco Meza, Carlos; Mhlanga, Sibaliso; Miake, Yasuo; Micheletti, Luca; Mieskolainen, Matti Mikael; Mihaylov, Dimitar Lubomirov; Mikhaylov, Konstantin; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Auro Prasad; Mohanty, Bedangadas; Khan, Mohammed Mohisin; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munning, Konstantin; Arratia Munoz, Miguel Ignacio; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Myers, Corey James; Myrcha, Julian Wojciech; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Narayan, Amrendra; Naru, Muhammad Umair; Nassirpour, Adrian Fereydon; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Negrao De Oliveira, Renato Aparecido; Nellen, Lukas; Nesbo, Simon Voigt; Neskovic, Gvozden; Ng, Fabian; Nicassio, Maria; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oh, Hoonjung; Ohlson, Alice Elisabeth; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Pachmayer, Yvonne Chiara; Pacik, Vojtech; Pagano, Davide; Paic, Guy; Palni, Prabhakar; Pan, Jinjin; Pandey, Ashutosh Kumar; Panebianco, Stefano; Papikyan, Vardanush; Pareek, Pooja; Park, Jonghan; Parkkila, Jasper Elias; Parmar, Sonia; Passfeld, Annika; Pathak, Surya Prakash; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Peng, Xinye; Pereira, Luis Gustavo; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrovici, Mihai; Petta, Catia; Peretti Pezzi, Rafael; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Pisano, Silvia; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pliquett, Fabian; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Poppenborg, Hendrik; Porteboeuf, Sarah Julie; Pozdniakov, Valeriy; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Punin, Valery; Putschke, Jorn Henning; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Ratza, Viktor; Ravasenga, Ivan; Read, Kenneth Francis; Redlich, Krzysztof; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reshetin, Andrey; Revol, Jean-pierre; Reygers, Klaus Johannes; Riabov, Viktor; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-lucian; Rode, Sudhir Pandurang; Rodriguez Cahuantzi, Mario; Roeed, Ketil; Rogalev, Roman; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Rokita, Przemyslaw Stefan; Ronchetti, Federico; Dominguez Rosas, Edgar; Roslon, Krystian; Rosnet, Philippe; Rossi, Andrea; Rotondi, Alberto; Roukoutakis, Filimon; Roy, Christelle Sophie; Roy, Pradip Kumar; Vazquez Rueda, Omar; Rui, Rinaldo; Rumyantsev, Boris; Rustamov, Anar; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Saha, Sumit Kumar; Sahoo, Baidyanath; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandoval, Andres; Sarkar, Amal; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Sas, Mike Henry Petrus; Scapparone, Eugenio; Scarlassara, Fernando; Schaefer, Brennan; Scheid, Horst Sebastian; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schmidt, Marten Ole; Schmidt, Martin; Schmidt, Nicolas Vincent; Schukraft, Jurgen; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sett, Priyanka; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shahoyan, Ruben; Shaikh, Wadut; Shangaraev, Artem; Sharma, Anjali; Sharma, Ankita; Sharma, Meenakshi; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shimomura, Maya; Shirinkin, Sergey; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singh, Randhir; Singhal, Vikas; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Soramel, Francesca; Sorensen, Soren Pontoppidan; Sozzi, Federica; Sputowska, Iwona Anna; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Stocco, Diego; Storetvedt, Maksim Melnik; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Suzuki, Ken; Swain, Sagarika; Szabo, Alexander; Szarka, Imrich; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terrevoli, Cristina; Teyssier, Boris; Thakur, Dhananjaya; Thakur, Sanchari; Thomas, Deepa; Thoresen, Freja; Tieulent, Raphael Noel; Tikhonov, Anatoly; Timmins, Anthony Robert; Toia, Alberica; Topilskaya, Nataliya; Toppi, Marco; Rojas Torres, Solangel; Tripathy, Sushanta; Trogolo, Stefano; Trombetta, Giuseppe; Tropp, Lukas; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Trzcinski, Tomasz Piotr; Trzeciak, Barbara Antonina; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Umaka, Ejiro Naomi; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vazquez Doce, Oton; Vechernin, Vladimir; Veen, Annelies Marianne; Vercellin, Ermanno; Vergara Limon, Sergio; Vermunt, Luuk; Vernet, Renaud; Vertesi, Robert; Vickovic, Linda; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Virgili, Tiziano; Vislavicius, Vytautas; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Voscek, Dominik; Vranic, Danilo; Vrlakova, Janka; Wagner, Boris; Wang, Hongkai; Wang, Mengliang; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Wegrzynek, Adam; Weiser, Dennis Franz; Wenzel, Sandro Christian; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Willems, Guido Alexander; Williams, Crispin; Willsher, Emily; Windelband, Bernd Stefan; Witt, William Edward; Xu, Ran; Yalcin, Serpil; Yamakawa, Kosei; Yano, Satoshi; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correa Zanoli, Henrique Jose; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zhalov, Mikhail; Zhang, Xiaoming; Zhang, Yonghong; Zhang, Zuman; Zhao, Chengxin; Zherebchevskii, Vladimir; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Ya; Zichichi, Antonino; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zmeskal, Johann; Zou, Shuguang

2018-01-01

Transverse momentum ($p_{\\rm T}$ ) spectra of charged particles at mid-pseudorapidity in Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$ TeV measured with the ALICE apparatus at the Large Hadron Collider are reported. The kinematic range $0.15 10$ GeV/$c$. This similarity is qualitatively consistent with the expected quadratic path length dependence of medium-induced radiative energy loss of a parton propagating in the medium. The centrality dependence of the ratio of the average transverse momentum $p_{\\rm T}$ in Xe-Xe collisions over Pb-Pb collisions at $\\sqrt{s_{\\rm NN}} = 5.02$ TeV is compared to hydrodynamical model calculations.

Application of a New Hybrid RANS/LES Modeling Paradigm to Compressible Flow

Science.gov (United States)

Oliver, Todd; Pederson, Clark; Haering, Sigfried; Moser, Robert

2017-11-01

It is well-known that traditional hybrid RANS/LES modeling approaches suffer from a number of deficiencies. These deficiencies often stem from overly simplistic blending strategies based on scalar measures of turbulence length scale and grid resolution and from use of isotropic subgrid models in LES regions. A recently developed hybrid modeling approach has shown promise in overcoming these deficiencies in incompressible flows [Haering, 2015]. In the approach, RANS/LES blending is accomplished using a hybridization parameter that is governed by an additional model transport equation and is driven to achieve equilibrium between the resolved and unresolved turbulence for the given grid. Further, the model uses an tensor eddy viscosity that is formulated to represent the effects of anisotropic grid resolution on subgrid quantities. In this work, this modeling approach is extended to compressible flows and implemented in the compressible flow solver SU2 (http://su2.stanford.edu/). We discuss both modeling and implementation challenges and show preliminary results for compressible flow test cases with smooth wall separation.

Status and modeling improvements of hybrid wind/PV/diesel power systems for Brazilian applications

Energy Technology Data Exchange (ETDEWEB)

McGowan, J.G.; Manwell, J.F.; Avelar, C. [Univ. of Massachusetts, Amherst, MA (United States); Taylor, R. [National Renewable Energy Lab., Golden, CO (United States)

1997-12-31

This paper present a summary of the ongoing work on the modeling and system design of hybrid wind/PV/diesel systems for two different sites in the Amazonia region of Brazil. The work incorporates the latest resource data and is based on the use of the Hybrid2 simulation code developed by the University of Massachusetts and NREL. Details of the baseline operating hybrid systems are reviewed, and the results of the latest detailed hybrid system evaluation for each site are summarized. Based on the system modeling results, separate recommendations for system modification and improvements are made.

On the Likely Utility of Hybrid Weights Optimized for Variances in Hybrid Error Covariance Models

Science.gov (United States)

Satterfield, E.; Hodyss, D.; Kuhl, D.; Bishop, C. H.

2017-12-01

Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble covariance is equal to the true error covariance of a forecast. Previous work demonstrated how information about the distribution of true error variances given an ensemble sample variance can be revealed from an archive of (observation-minus-forecast, ensemble-variance) data pairs. Here, we derive a simple and intuitively compelling formula to obtain the mean of this distribution of true error variances given an ensemble sample variance from (observation-minus-forecast, ensemble-variance) data pairs produced by a single run of a data assimilation system. This formula takes the form of a Hybrid weighted average of the climatological forecast error variance and the ensemble sample variance. Here, we test the extent to which these readily obtainable weights can be used to rapidly optimize the covariance weights used in Hybrid data assimilation systems that employ weighted averages of static covariance models and flow-dependent ensemble based covariance models. Univariate data assimilation and multi-variate cycling ensemble data assimilation are considered. In both cases, it is found that our computationally efficient formula gives Hybrid weights that closely approximate the optimal weights found through the simple but computationally expensive process of testing every plausible combination of weights.

On prognostic models, artificial intelligence and censored observations.

Science.gov (United States)

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.

Breakdown of NpNn scheme in very heavy nuclei

International Nuclear Information System (INIS)

Varshney, A.K.; Singh, M.; Kumar, Rajesh; Gupta, K.K.; Gupta, D.K.

2016-01-01

The proton neutron interaction has been considered the key ingredient in the development of configuration mixing, collectivity and ultimately deformation in atomic nuclei for over five decades. Phenomenologically, the correlation of the integrated valance p - n interaction with the onset of collectivity and deformation has been described in terms of NpNn scheme

Parametric Linear Hybrid Automata for Complex Environmental Systems Modeling

Directory of Open Access Journals (Sweden)

Samar Hayat Khan Tareen

2015-07-01

Full Text Available Environmental systems, whether they be weather patterns or predator-prey relationships, are dependent on a number of different variables, each directly or indirectly affecting the system at large. Since not all of these factors are known, these systems take on non-linear dynamics, making it difficult to accurately predict meaningful behavioral trends far into the future. However, such dynamics do not warrant complete ignorance of different efforts to understand and model close approximations of these systems. Towards this end, we have applied a logical modeling approach to model and analyze the behavioral trends and systematic trajectories that these systems exhibit without delving into their quantification. This approach, formalized by René Thomas for discrete logical modeling of Biological Regulatory Networks (BRNs and further extended in our previous studies as parametric biological linear hybrid automata (Bio-LHA, has been previously employed for the analyses of different molecular regulatory interactions occurring across various cells and microbial species. As relationships between different interacting components of a system can be simplified as positive or negative influences, we can employ the Bio-LHA framework to represent different components of the environmental system as positive or negative feedbacks. In the present study, we highlight the benefits of hybrid (discrete/continuous modeling which lead to refinements among the fore-casted behaviors in order to find out which ones are actually possible. We have taken two case studies: an interaction of three microbial species in a freshwater pond, and a more complex atmospheric system, to show the applications of the Bio-LHA methodology for the timed hybrid modeling of environmental systems. Results show that the approach using the Bio-LHA is a viable method for behavioral modeling of complex environmental systems by finding timing constraints while keeping the complexity of the model

Event-by-event fluctuations in mean pT and mean eT in √(sNN)=130 GeV Au+Au collisions

International Nuclear Information System (INIS)

Adcox, K.; El Chenawi, K.; Ghosh, T.K.; Greene, S.V.; Maguire, C.F.; Miller, T.E.; Rose, A.A.; Adler, S.S.; Aronson, S.H.; David, G.; Desmond, E.J.; Ewell, L.; Franz, A.; Guryn, W.; Haggerty, J.S.; Johnson, B.M.; Kistenev, E.; Kroon, P.J.; Mahon, J.; Makdisi, Y.I.

2002-01-01

Distributions of event-by-event fluctuations of the mean transverse momentum and mean transverse energy near mid-rapidity have been measured in Au+Au collisions at √(s NN )=130 GeV at the Relativistic Heavy-Ion Collider. By comparing the distributions to what is expected for statistically independent particle emission, the magnitude of nonstatistical fluctuations in mean transverse momentum is determined to be consistent with zero. Also, no significant nonrandom fluctuations in mean transverse energy are observed. By constructing a fluctuation model with two event classes that preserve the mean and variance of the semi-inclusive p T or e T spectra, we exclude a region of fluctuations in √(s NN )=130 GeV Au+Au collisions

Precise strength of the $\\pi$NN coupling constant

CERN Document Server

Ericson, Torleif Eric Oskar; Rahm, J; Blomgren, J; Olsson, N; Thomas, A W

1998-01-01

We report here a preliminary value for the piNN coupling constant deduced from the GMO sumrule for forward piN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives $g^2_c$(GMO) = 13.99(24).

Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposaL

Energy Technology Data Exchange (ETDEWEB)

Wichapa, Narong; Khokhajaikiat, Porntep

2017-07-01

Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposaL

International Nuclear Information System (INIS)

Wichapa, Narong; Khokhajaikiat, Porntep

2017-01-01

Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid

Directory of Open Access Journals (Sweden)

Zhiyong Lin

2017-11-01

Full Text Available The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and query efficiency, but ignores the spatial location information. The grid model retains the plane position information by grid, but increases the data volume and complexity of the model and reduces the efficiency of the model analysis. This paper presents a hybrid model for interior space based on topology and grid. Based on the spatial meshing and spatial division of the interior space, the model retains the position information and topological connectivity information of the interior space by establishing the connection or affiliation between the grid subspace and the topological subspace. The model improves the speed of interior spatial analysis and solves the problem of the topology information and location information updates not being synchronized. In this study, the A* shortest path query efficiency of typical daily indoor activities under the grid model and the hybrid model were compared for the indoor plane of an apartment and a shopping mall. The results obtained show that the hybrid model is 43% higher than the A* algorithm of the grid model as a result of the existence of topology communication information. This paper provides a useful idea for the establishment of a highly efficient and highly available interior spatial data model.

Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility

Science.gov (United States)

Tuba, Zoltán; Bottyán, Zsolt

2018-04-01

Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.

Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM)

Science.gov (United States)

Halliwell, George R.

Vertical coordinate and vertical mixing algorithms included in the HYbrid Coordinate Ocean Model (HYCOM) are evaluated in low-resolution climatological simulations of the Atlantic Ocean. The hybrid vertical coordinates are isopycnic in the deep ocean interior, but smoothly transition to level (pressure) coordinates near the ocean surface, to sigma coordinates in shallow water regions, and back again to level coordinates in very shallow water. By comparing simulations to climatology, the best model performance is realized using hybrid coordinates in conjunction with one of the three available differential vertical mixing models: the nonlocal K-Profile Parameterization, the NASA GISS level 2 turbulence closure, and the Mellor-Yamada level 2.5 turbulence closure. Good performance is also achieved using the quasi-slab Price-Weller-Pinkel dynamical instability model. Differences among these simulations are too small relative to other errors and biases to identify the "best" vertical mixing model for low-resolution climate simulations. Model performance deteriorates slightly when the Kraus-Turner slab mixed layer model is used with hybrid coordinates. This deterioration is smallest when solar radiation penetrates beneath the mixed layer and when shear instability mixing is included. A simulation performed using isopycnic coordinates to emulate the Miami Isopycnic Coordinate Ocean Model (MICOM), which uses Kraus-Turner mixing without penetrating shortwave radiation and shear instability mixing, demonstrates that the advantages of switching from isopycnic to hybrid coordinates and including more sophisticated turbulence closures outweigh the negative numerical effects of maintaining hybrid vertical coordinates.

A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

Directory of Open Access Journals (Sweden)

Bin Jia

2017-01-01

Full Text Available The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR, accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN, and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

Pedagogy and Process: A Case Study of Writing in a Hybrid Learning Model

Science.gov (United States)

Keiner, Jason F.

2017-01-01

This qualitative case study explored the perceived experiences and outcomes of writing in a hybrid model of instruction in a large suburban high school. In particular, the impact of a hybrid model on the writing process and on future writing performance were examined. In addition, teacher expectation and teacher attitude and their impact upon…

Modelling and Optimising the Value of a Hybrid Solar-Wind System

Science.gov (United States)

Nair, Arjun; Murali, Kartik; Anbuudayasankar, S. P.; Arjunan, C. V.

2017-05-01

In this paper, a net present value (NPV) approach for a solar hybrid system has been presented. The system, in question aims at supporting an investor by assessing an investment in solar-wind hybrid system in a given area. The approach follow a combined process of modelling the system, with optimization of major investment-related variables to maximize the financial yield of the investment. The consideration of solar wind hybrid supply presents significant potential for cost reduction. The investment variables concern the location of solar wind plant, and its sizing. The system demand driven, meaning that its primary aim is to fully satisfy the energy demand of the customers. Therefore, the model is a practical tool in the hands of investor to assess and optimize in financial terms an investment aiming at covering real energy demand. Optimization is performed by taking various technical, logical constraints. The relation between the maximum power obtained between individual system and the hybrid system as a whole in par with the net present value of the system has been highlighted.

Charged-particle multiplicity density at mid-rapidity in central Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

CERN Document Server

Aamodt, K; Abrahantes Quintana, A; Adamova, D; Adare, A M; Aggarwal, M M; Aglieri Rinella, G; Agocs, A G; Aguilar Salazar, S; Ahammed, Z; Ahmad Masoodi, A; Ahmad, N; Ahn, S U; Akindinov, A; Aleksandrov, D; Alessandro, B; Alfaro Molina, R; Alici, A; Alkin, A; Almaraz Avina, E; Alt, T; Altini, V; Altinpinar, S; Altsybeev, I; Andrei, C; Andronic, A; Anguelov, V; Anson, C; Anticic, T; Antinori, F; Antonioli, P; Aphecetche, L; Appelshauser, H; Arbor, N; Arcelli, S; Arend, A; Armesto, N; Arnaldi, R; Aronsson, T; Arsene, I C; Asryan, A; Augustinus, A; Averbeck, R; Awes, T C; Aysto, J; Azmi, M D; Bach, M; Badala, A; Baek, Y W; Bagnasco, S; Bailhache, R; Bala, R; Baldini-Ferroli, R; Baldisseri, A; Baldit, A; Baltasar Dos Santos Pedrosa, F; Ban, J; Barbera, R; Barile, F; Barnafoldi, G G; Barnby, L S; Barret, V; Bartke, J; Basile, M; Bastid, N; Bathen, B; Batigne, G; Batyunya, B; Baumann, C; Bearden, I G; Beck, H; Belikov, I; Bellini, F; Bellwied, R; Belmont-Moreno, E; Beole, S; Berceanu, I; Bercuci, A; Berdermann, E; Berdnikov, Y; Bergmann, C; Betev, L; Bhasin, A; Bhati, A K; Bianchi, L; Bianchi, N; Bianchin, C; Bielcik, J; Bielcikova, J; Bilandzic, A; Biolcati, E; Blanc, A; Blanco, F; Blanco, F; Blau, D; Blume, C; Boccioli, M; Bock, N; Bogdanov, A; Boggild, H; Bogolyubsky, M; Boldizsar, L; Bombara, M; Bombonati, C; Book, J; Borel, H; Borissov, A; Bortolin, C; Bose, S; Bossu, F; Botje, M; Bottger, S; Boyer, B; Braun-Munzinger, P; Bravina, L; Bregant, M; Breitner, T; Broz, M; Brun, R; Bruna, E; Bruno, G E; Budnikov, D; Buesching, H; Bugaiev, K; Busch, O; Buthelezi, Z; Caffarri, D; Cai, X; Caines, H; Calvo Villar, E; Camerini, P; Canoa Roman, V; Cara Romeo, G; Carena, F; Carena, W; Carminati, F; Casanova Diaz, A; Caselle, M; Castillo Castellanos, J; Catanescu, V; Cavicchioli, C; Cepila, J; Cerello, P; Chang, B; Chapeland, S; Charvet, J L; Chattopadhyay, S; Chattopadhyay, S; Cherney, M; Cheshkov, C; Cheynis, B; Chiavassa, E; Chibante Barroso, V; Chinellato, D D; Chochula, P; Chojnacki, M; Christakoglou, P; Christensen, C H; Christiansen, P; Chujo, T; Cicalo, C; Cifarelli, L; Cindolo, F; Cleymans, J; Coccetti, F; Coffin, J P; Coli, S; Conesa Balbastre, G; Conesa del Valle, Z; Constantin, P; Contin, G; Contreras, J G; Cormier, T M; Corrales Morales, Y; Cortes Maldonado, I; Cortese, P; Cosentino, M R; Costa, F; Cotallo, M E; Crescio, E; Crochet, P; Cuautle, E; Cunqueiro, L; D'Erasmo, G; Dainese, A; Dalsgaard, H H; Danu, A; Das, D; Das, I; Das, K; Dash, A; Dash, S; De, S; De Azevedo Moregula, A; de Barros, G O V; De Caro, A; de Cataldo, G; de Cuveland, J; De Falco, A; De Gruttola, D; De Marco, N; De Pasquale, S; De Remigis, R; de Rooij, R; Debski, P R; Del Castillo Sanchez, E; Delagrange, H; Delgado Mercado, Y; Dellacasa, G; Deloff, A; Demanov, V; Denes, E; Deppman, A; Di Bari, D; Di Giglio, C; Di Liberto, S; Di Mauro, A; Di Nezza, P; Dietel, T; Divia, R; Djuvsland, O; Dobrin, A; Dobrowolski, T; Dominguez, I; Donigus, B; Dordic, O; Driga, O; Dubey, A K; Dubuisson, J; Ducroux, L; Dupieux, P; Dutta Majumdar, A K; Dutta Majumdar, M R; Elia, D; Emschermann, D; Engel, H; Erdal, H A; Espagnon, B; Estienne, M; Esumi, S; Evans, D; Evrard, S; Eyyubova, G; Fabjan, C W; Fabris, D; Faivre, J; Falchieri, D; Fantoni, A; Fasel, M; Fearick, R; Fedunov, A; Fehlker, D; Fekete, V; Felea, D; Feofilov, G; Fernandez Tellez, A; Ferretti, A; Ferretti, R; Figiel, J; Figueredo, M A S; Filchagin, S; Fini, R; Finogeev, D; Fionda, F M; Fiore, E M; Floris, M; Foertsch, S; Foka, P; Fokin, S; Fragiacomo, E; Fragkiadakis, M; Frankenfeld, U; Fuchs, U; Furano, F; Furget, C; Fusco Girard, M; Gaardhoje, J J; Gadrat, S; Gagliardi, M; Gago, A; Gallio, M; Gangadharan, D R; Ganoti, P; Ganti, M S; Garabatos, C; Garcia-Solis, E; Garishvili, I; Gemme, R; Gerhard, J; Germain, M; Geuna, C; Gheata, A; Gheata, M; Ghidini, B; Ghosh, P; Gianotti, P; Girard, M R; Giraudo, G; Giubellino, P; Gladysz-Dziadus, E; Glassel, P; Gomez, R; Ferreiro, E G; Gonzalez Santos, H; González-Trueba, L H; González-Zamora, P; Gorbunov, S; Gotovac, S; Grabski, V; Grajcarek, R; Grelli, A; Grigoras, A; Grigoras, C; Grigoriev, V; Grigoryan, A; Grigoryan, S; Grinyov, B; Grion, N; Gros, P; Grosse-Oetringhaus, J F; Grossiord, J Y; Grosso, R; Guber, F; Guernane, R; Guerra Gutierrez, C; Guerzoni, B; Gulbrandsen, K; Gunji, T; Gupta, A; Gupta, R; Gutbrod, H; Haaland, O; Hadjidakis, C; Haiduc, M; Hamagaki, H; Hamar, G; Harris, J W; Hartig, M; Hasch, D; Hasegan, D; Hatzifotiadou, D; Hayrapetyan, A; Heide, M; Heinz, M; Helstrup, H; Herghelegiu, A; Hernandez, C; Herrera Corral, G; Herrmann, N; Hetland, K F; Hicks, B; Hille, P T; Hippolyte, B; Horaguchi, T; Hori, Y; Hristov, P; Hrivnacova, I; Huang, M; Huber, S; Humanic, T J; Hwang, D S; Ichou, R; Ilkaev, R; Ilkiv, I; Inaba, M; Incani, E; Innocenti, G M; Innocenti, P G; Ippolitov, M; Irfan, M; Ivan, C; Ivanov, A; Ivanov, M; Ivanov, V; Jacholkowski, A; Jacobs, P M; Jancurova, L; Jangal, S; Janik, R; Jena, S; Jirden, L; Jones, G T; Jones, P G; Jovanovic, P; Jung, H; Jung, W; Jusko, A; Kalcher, S; Kalinak, P; Kalisky, M; Kalliokoski, T; Kalweit, A; Kamermans, R; Kanaki, K; Kang, E; Kang, J H; Kaplin, V; Karavichev, O; Karavicheva, T; Karpechev, E; Kazantsev, A; Kebschull, U; Keidel, R; Khan, M M; Khan, S A; Khanzadeev, A; Kharlov, Y; Kileng, B; Kim, D J; Kim, D S; Kim, D W; Kim, H N; Kim, J H; Kim, J S; Kim, M; Kim, M; Kim, S; Kim, S H; Kirsch, S; Kisel, I; Kiselev, S; Kisiel, A; Klay, J L; Klein, J; Klein-Bosing, C; Kliemant, M; Klovning, A; Kluge, A; Knichel, M L; Koch, K; Kohler, M; Kolevatov, R; Kolojvari, A; Kondratiev, V; Kondratyeva, N; Konevskih, A; Kornas, E; Kottachchi Kankanamge Don, C; Kour, R; Kowalski, M; Kox, S; Koyithatta Meethaleveedu, G; Kozlov, K; Kral, J; Kralik, I; Kramer, F; Kraus, I; Krawutschke, T; Kretz, M; Krivda, M; Krizek, F; Krumbhorn, D; Krus, M; Kryshen, E; Krzewicki, M; Kucheriaev, Y; Kuhn, C; Kuijer, P G; Kurashvili, P; Kurepin, A; Kurepin, A B; Kuryakin, A; Kushpil, S; Kushpil, V; Kweon, M J; Kwon, Y; La Rocca, P; Ladron de Guevara, P; Lafage, V; Lara, C; Lardeux, A; Larsen, D T; Lazzeroni, C; Le Bornec, Y; Lea, R; Lee, K S; Lee, S C; Lefevre, F; Lehnert, J; Leistam, L; Lenhardt, M; Lenti, V; Leon Monzon, I; Leon Vargas, H; Levai, P; Li, X; Lien, J; Lietava, R; Lindal, S; Lindenstruth, V; Lippmann, C; Lisa, M A; Liu, L; Loenne, P I; Loggins, V R; Loginov, V; Lohn, S; Loizides, C; Loo, K K; Lopez, X; Lopez Noriega, M; Lopez Torres, E; Lovhoiden, G; Lu, X G; Luettig, P; Lunardon, M; Luparello, G; Luquin, L; Luzzi, C; Ma, K; Ma, R; Madagodahettige-Don, D M; Maevskaya, A; Mager, M; Mahapatra, D P; Maire, A; Mal'Kevich, D; Malaev, M; Maldonado Cervantes, I; Malinina, L; Malzacher, P; Mamonov, A; Manceau, L; Mangotra, L; Manko, V; Manso, F; Manzari, V; Mao, Y; Mares, J; Margagliotti, G V; Margotti, A; Marin, A; Markert, C; Martashvili, I; Martinengo, P; Martinez, M I; Martinez Davalos, A; Martinez Garcia, G; Martynov, Y; Masciocchi, S; Masera, M; Masoni, A; Massacrier, L; Mastromarco, M; Mastroserio, A; Matthews, Z L; Matyja, A; Mayani, D; Mayer, C; Mazza, G; Mazzoni, M A; Meddi, F; Menchaca-Rocha, A; Mendez Lorenzo, P; Menis, I; Mercado Perez, J; Meres, M; Mereu, P; Miake, Y; Midori, J; Milano, L; Milosevic, J; Mischke, A; Miskowiec, D; Mitu, C; Mlynarz, J; Mohanty, A K; Mohanty, B; Molnar, L; Montano Zetina, L; Monteno, M; Montes, E; Morando, M; Moreira De Godoy, D A; Moretto, S; Morsch, A; Muccifora, V; Mudnic, E; Muhuri, S; Muller, H; Munhoz, M G; Munoz, J; Musa, L; Musso, A; Nandi, B K; Nania, R; Nappi, E; Nattrass, C; Navach, F; Navin, S; Nayak, T K; Nazarenko, S; Nazarov, G; Nedosekin, A; Nendaz, F; Newby, J; Nicassio, M; Nielsen, B S; Niida, T; Nikolaev, S; Nikolic, V; Nikulin, S; Nikulin, V; Nilsen, B S; Nilsson, M S; Noferini, F; Nooren, G; Novitzky, N; Nyanin, A; Nyatha, A; Nygaard, C; Nystrand, J; Obayashi, H; Ochirov, A; Oeschler, H; Oh, S K; Oleniacz, J; Oppedisano, C; Ortiz Velasquez, A; Ortona, G; Oskarsson, A; Ostrowski, P; Otterlund, I; Otwinowski, J; Oyama, K; Ozawa, K; Pachmayer, Y; Pachr, M; Padilla, F; Pagano, P; Jayarathna, S P; Paic, G; Painke, F; Pajares, C; Pal, S; Pal, S K; Palaha, A; Palmeri, A; Pappalardo, G S; Park, W J; Patalakha, D I; Paticchio, V; Pavlinov, A; Pawlak, T; Peitzmann, T; Peresunko, D; Perez Lara, C E; Perini, D; Perrino, D; Peryt, W; Pesci, A; Peskov, V; Pestov, Y; Peters, A J; Petracek, V; Petran, M; Petris, M; Petrov, P; Petrovici, M; Petta, C; Piano, S; Piccotti, A; Pikna, M; Pillot, P; Pinazza, O; Pinsky, L; Pitz, N; Piuz, F; Piyarathna, D B; Platt, R; Ploskon, M; Pluta, J; Pocheptsov, T; Pochybova, S; Podesta-Lerma, P L M; Poghosyan, M G; Polak, K; Polichtchouk, B; Pop, A; Porteboeuf, S; Pospisil, V; Potukuchi, B; Prasad, S K; Preghenella, R; Prino, F; Pruneau, C A; Pshenichnov, I; Puddu, G; Pulvirenti, A; Punin, V; Putis, M; Putschke, J; Quercigh, E; Qvigstad, H; Rachevski, A; Rademakers, A; Rademakers, O; Radomski, S; Raiha, T S; Rak, J; Rakotozafindrabe, A; Ramello, L; Ramirez Reyes, A; Rammler, M; Raniwala, R; Raniwala, S; Rasanen, S S; Read, K F; Real, J; Redlich, K; Renfordt, R; Reolon, A R; Reshetin, A; Rettig, F; Revol, J P; Reygers, K; Ricaud, H; Riccati, L; Ricci, R A; Richter, M; Riedler, P; Riegler, W; Riggi, F; Rodriguez Cahuantzi, M; Rohr, D; Rohrich, D; Romita, R; Ronchetti, F; Rosinsky, P; Rosnet, P; Rossegger, S; Rossi, A; Roukoutakis, F; Rousseau, S; Roy, C; Roy, P; Rubio Montero, A J; Rui, R; Rivetti, A; Rusanov, I; Ryabinkin, E; Rybicki, A; Sadovsky, S; Safarik, K; Sahoo, R; Sahu, P K; Saini, J; Saiz, P; Sakai, S; Sakata, D; Salgado, C A; Samanta, T; Sambyal, S; Samsonov, V; Sanchez Castro, X; Sandor, L; Sandoval, A; Sano, M; Sano, S; Santo, R; Santoro, R; Sarkamo, J; Saturnini, P; Scapparone, E; Scarlassara, F; Scharenberg, R P; Schiaua, C; Schicker, R; Schmidt, C; Schmidt, H R; Schreiner, S; Schuchmann, S; Schukraft, J; Schutz, Y; Schwarz, K; Schweda, K; Scioli, G; Scomparin, E; Scott, P A; Scott, R; Segato, G; Selyuzhenkov, I; Senyukov, S; Seo, J; Serci, S; Serradilla, E; Sevcenco, A; Sgura, I; Shabratova, G; Shahoyan, R; Sharma, N; Sharma, S; Shigaki, K; Shimomura, M; Shtejer, K; Sibiriak, Y; Siciliano, M; Sicking, E; Siemiarczuk, T; Silenzi, A; Silvermyr, D; Simonetti, G; Singaraju, R; Singh, R; Singhal, V; Sinha, B C; Sinha, T; Sitar, B; Sitta, M; Skaali, T B; Skjerdal, K; Smakal, R; Smirnov, N; Snellings, R; Sogaard, C; Soloviev, A; Soltz, R; Son, H; Song, J; Song, M; Soos, C; Soramel, F; Spyropoulou-Stassinaki, M; Srivastava, B K; Stachel, J; Stan, I; Stefanek, G; Stefanini, G; Steinbeck, T; Steinpreis, M; Stenlund, E; Steyn, G; Stocco, D; Stock, R; Stokkevag, C H; Stolpovskiy, M; Strmen, P; Suaide, A A P; Subieta Vasquez, M A; Sugitate, T; Suire, C; Sukhorukov, M; Sumbera, M; Susa, T; Swoboda, D; Symons, T J M; Szanto de Toledo, A; Szarka, I; Szostak, A; Tagridis, C; Takahashi, J; Tapia Takaki, J D; Tauro, A; Tavlet, M; Tejeda Munoz, G; Telesca, A; Terrevoli, C; Thader, J; Thomas, D; Thomas, J H; Tieulent, R; Timmins, A R; Tlusty, D; Toia, A; Torii, H; Toscano, L; Tosello, F; Traczyk, T; Truesdale, D; Trzaska, W H; Tsuji, T; Tumkin, A; Turrisi, R; Turvey, A J; Tveter, T S; Ulery, J; Ullaland, K; Uras, A; Urban, J; Urciuoli, G M; Usai, G L; Vacchi, A; Vajzer, M; Vala, M; Valencia Palomo, L; Vallero, S; van der Kolk, N; van Leeuwen, M; Vande Vyvre, P; Vannucci, L; Vargas, A; Varma, R; Vasileiou, M; Vasiliev, A; Vechernin, V; Veldhoen, M; Venaruzzo, M; Vercellin, E; Vergara, S; Vernekohl, D C; Vernet, R; Verweij, M; Vickovic, L; Viesti, G; Vikhlyantsev, O; Vilakazi, Z; Villalobos Baillie, O; Vinogradov, A; Vinogradov, L; Vinogradov, Y; Virgili, T; Viyogi, Y P; Vodopyanov, A; Voloshin, K; Voloshin, S; Volpe, G; von Haller, B; Vranic, D; Ovrebekk, G; Vrlakova, J; Vulpescu, B; Vyushin, A; Wagner, B; Wagner, V; Wan, R; Wang, D; Wang, Y; Wang, Y; Watanabe, K; Wessels, J P; Westerhoff, U; Wiechula, J; Wikne, J; Wilde, M; Wilk, A; Wilk, G; Williams, M C S; Windelband, B; Xaplanteris Karampatsos, L; Yang, H; Yang, S; Yasnopolskiy, S; Yi, J; Yin, Z; Yokoyama, H; Yoo, I K; Yu, W; Yuan, X; Yushmanov, I; Zabrodin, E; Zach, C; Zampolli, C; Zaporozhets, S; Zarochentsev, A; Zavada, P; Zaviyalov, N; Zbroszczyk, H; Zelnicek, P; Zenin, A; Zgura, I; Zhalov, M; Zhang, X; Zhou, D; Zichichi, A; Zinovjev, G; Zoccarato, Y; Zynovyev, M

2010-01-01

The first measurement of the charged-particle multiplicity density at mid-rapidity in Pb-Pb collisions at a centre-of-mass energy per nucleon pair sqrt(sNN) = 2.76 TeV is presented. For an event sample corresponding to the most central 5% of the hadronic cross section the pseudo-rapidity density of primary charged particles at mid-rapidity is 1584 +- 4 (stat) +- 76 (sys.), which corresponds to 8.3 +- 0.4 (sys.) per participating nucleon pair. This represents an increase of about a factor 1.9 relative to pp collisions at similar collision energies, and about a factor 2.2 to central Au-Au collisions at sqrt(sNN) = 0.2 TeV. This measurement provides the first experimental constraint for models of nucleus-nucleus collisions at LHC energies.

Role of rho exchange in isobar contributions to the NN interaction

International Nuclear Information System (INIS)

Bagnoud, X.; Holinde, K.; Machleidt, R.

1984-01-01

The fourth-order noniterative diagrams of πrho exchange involving nucleon-isobar intermediate states are evaluated in momentum space in the framework of noncovariant perturbation theory. It is shown that the sum of all time orderings (iterative plus noniterative) can be reasonably well approximated by twice the isoscalar piece of the iterative ones (which are much simpler to evaluate). The same approximation is used in order to describe the sum of all time orderings for the corresponding diagrams involving double-isobar intermediate states. The role of these contributions is studied in NN scattering. Especially, it is investigated whether such contributions can quantitatively replace part of the ω-exchange contribution used in one-boson-exchange models

Fluid Survival Tool: A Model Checker for Hybrid Petri Nets

NARCIS (Netherlands)

Postema, Björn Frits; Remke, Anne Katharina Ingrid; Haverkort, Boudewijn R.H.M.; Ghasemieh, Hamed

2014-01-01

Recently, algorithms for model checking Stochastic Time Logic (STL) on Hybrid Petri nets with a single general one-shot transition (HPNG) have been introduced. This paper presents a tool for model checking HPNG models against STL formulas. A graphical user interface (GUI) not only helps to

Hybrid Adaptive Flight Control with Model Inversion Adaptation

Science.gov (United States)

Nguyen, Nhan

2011-01-01

This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

Hybrid ATDL-gamma distribution model for predicting area source acid gas concentrations

Energy Technology Data Exchange (ETDEWEB)

Jakeman, A J; Taylor, J A

1985-01-01

An air quality model is developed to predict the distribution of concentrations of acid gas in an urban airshed. The model is hybrid in character, combining reliable features of a deterministic ATDL-based model with statistical distributional approaches. The gamma distribution was identified from a range of distributional models as the best model. The paper shows that the assumptions of a previous hybrid model may be relaxed and presents a methodology for characterizing the uncertainty associated with model predictions. Results are demonstrated for the 98-percentile predictions of 24-h average data over annual periods at six monitoring sites. This percentile relates to the World Health Organization goal for acid gas concentrations.

A computational model for lower hybrid current drive

International Nuclear Information System (INIS)

Englade, R.C.; Bonoli, P.T.; Porkolab, M.

1983-01-01

A detailed simulation model for lower hybrid (LH) current drive in toroidal devices is discussed. This model accounts reasonably well for the magnitude of radio frequency (RF) current observed in the PLT and Alcator C devices. It also reproduces the experimental dependencies of RF current generation on toroidal magnetic field and has provided insights about mechanisms which may underlie the observed density limit of current drive. (author)

Hierarchical models and iterative optimization of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)

2016-06-08

A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

The NN and NantiN interactions

International Nuclear Information System (INIS)

Vinh Mau, R.

1981-01-01

The present status of the low and medium energy NN interaction and of the low energy NantiN interaction is reviewed. Careful confrontation of theoretical predictions with the most recent results on experimental observables is emphasized. The question of the dibaryon resonances is discussed. For the NantiN interaction, in view of the problem of the existence of baryonium states as bound states or resonant states of the NantiN system and of their properties, tests of different types of annihilation potentials against the existing experimental data are examined. Implications for the future experimental program at LEAR are discussed

The Cheshire Cat principle for hybrid bag models

International Nuclear Information System (INIS)

Nielsen, H.B.

1987-05-01

The Cheshire Cat point of view where the bag in the chiral bag model has no physical significance, but only a notational one is argued for. It is explained how a fermion - in, say, a 1+1 dimensional exact Cheshire Cat model - escapes the bag by means of an anomaly. The possibility to construct sophisticated hybrid bag models is suggested which use the lack of physical significance of the bag to fix the many parameters so as to anyway give hope of a phenomenologically sensible model. (orig.)

A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries

International Nuclear Information System (INIS)

Suri, Girish; Onori, Simona

2016-01-01

In this paper, a semi-empirical Lithium-iron phosphate-graphite battery aging model is identified over data mimicking actual cycling conditions that a hybrid electric vehicle battery encounters under real driving scenarios. The aging model is then used to construct the severity factor map, used to characterize relative aging of the battery under different operating conditions. This is used as a battery degradation criterion within a multi-objective optimization problem where battery aging minimization is to be achieved along with fuel consumption minimization. The method proposed is general and can be applied to other battery chemistry as well as different vehicular applications. Finally, simulations conducted using a hybrid electric vehicle simulator show how the two modeling tools developed in this paper, i.e., the severity factor map and the aging model, can be effectively used in a multi-objective optimization problem to predict and control battery degradation. - Highlights: • Battery aging model for hybrid electric vehicles using real driving conditions data. • Development of a modeling tool to assess battery degradation for real time optimization. • "3"1P NMR analysis of an enzyme-treated extract showed expected hydrolysis of P forms. • Development of an energy management strategy to minimize battery degradation. • Simulation results from hybrid electric vehicle simulator.

Modeling and Simulation of Multi-scale Environmental Systems with Generalized Hybrid Petri Nets

Directory of Open Access Journals (Sweden)

Mostafa eHerajy

2015-07-01

Full Text Available Predicting and studying the dynamics and properties of environmental systems necessitates the construction and simulation of mathematical models entailing different levels of complexities. Such type of computational experiments often require the combination of discrete and continuous variables as well as processes operating at different time scales. Furthermore, the iterative steps of constructing and analyzing environmental models might involve researchers with different background. Hybrid Petri nets may contribute in overcoming such challenges as they facilitate the implementation of systems integrating discrete and continuous dynamics. Additionally, the visual depiction of model components will inevitably help to bridge the gap between scientists with distinct expertise working on the same problem. Thus, modeling environmental systems with hybrid Petri nets enables the construction of complex processes while keeping the models comprehensible for researchers working on the same project with significantly divergent education path. In this paper we propose the utilization of a special class of hybrid Petri nets, Generalized Hybrid Petri Nets (GHPN, to model and simulate environmental systems exposing processes interacting at different time-scales. GHPN integrate stochastic and deterministic semantics as well as other types of special basic events. Moreover, a case study is presented to illustrate the use of GHPN in constructing and simulating multi-timescale environmental scenarios.

Static stiffness modeling of a novel hybrid redundant robot machine

International Nuclear Information System (INIS)

Li Ming; Wu Huapeng; Handroos, Heikki

2011-01-01

This paper presents a modeling method to study the stiffness of a hybrid serial-parallel robot IWR (Intersector Welding Robot) for the assembly of ITER vacuum vessel. The stiffness matrix of the basic element in the robot is evaluated using matrix structural analysis (MSA); the stiffness of the parallel mechanism is investigated by taking account of the deformations of both hydraulic limbs and joints; the stiffness of the whole integrated robot is evaluated by employing the virtual joint method and the principle of virtual work. The obtained stiffness model of the hybrid robot is analytical and the deformation results of the robot workspace under certain external load are presented.

Evaluation of models generated via hybrid evolutionary algorithms ...

African Journals Online (AJOL)

2016-04-02

Apr 2, 2016 ... Evaluation of models generated via hybrid evolutionary algorithms for the prediction of Microcystis ... evolutionary algorithms (HEA) proved to be highly applica- ble to the hypertrophic reservoirs of South Africa. .... discovered and optimised using a large-scale parallel computational device and relevant soft-.

Nuclear Hybrid Energy Systems FY16 Modeling Efforts at ORNL

Energy Technology Data Exchange (ETDEWEB)

Cetiner, Sacit M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Greenwood, Michael Scott [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Harrison, Thomas J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Qualls, A. L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Guler Yigitoglu, Askin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fugate, David W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2016-09-01

A nuclear hybrid system uses a nuclear reactor as the basic power generation unit. The power generated by the nuclear reactor is utilized by one or more power customers as either thermal power, electrical power, or both. In general, a nuclear hybrid system will couple the nuclear reactor to at least one thermal power user in addition to the power conversion system. The definition and architecture of a particular nuclear hybrid system is flexible depending on local markets needs and opportunities. For example, locations in need of potable water may be best served by coupling a desalination plant to the nuclear system. Similarly, an area near oil refineries may have a need for emission-free hydrogen production. A nuclear hybrid system expands the nuclear power plant from its more familiar central power station role by diversifying its immediately and directly connected customer base. The definition, design, analysis, and optimization work currently performed with respect to the nuclear hybrid systems represents the work of three national laboratories. Idaho National Laboratory (INL) is the lead lab working with Argonne National Laboratory (ANL) and Oak Ridge National Laboratory. Each laboratory is providing modeling and simulation expertise for the integration of the hybrid system.

Complete modeling and software implementation of a virtual solar hydrogen hybrid system

International Nuclear Information System (INIS)

Pedrazzi, S.; Zini, G.; Tartarini, P.

2010-01-01

A complete mathematical model and software implementation of a solar hydrogen hybrid system has been developed and applied to real data. The mathematical model has been derived from sub-models taken from literature with appropriate modifications and improvements. The model has been implemented as a stand-alone virtual energy system in a model-based, multi-domain software environment. A test run has then been performed on typical residential user data-sets over a year-long period. Results show that the virtual hybrid system can bring about complete grid independence; in particular, hydrogen production balance is positive (+1.25 kg) after a year's operation with a system efficiency of 7%.

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

Directory of Open Access Journals (Sweden)

Chengdong Li

2018-01-01

Full Text Available To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity. In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN, the generalized radial basis function neural network (GRBFNN, the extreme learning machine (ELM, and the support vector regressor (SVR are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption

Precise strength of the πNN coupling constant

International Nuclear Information System (INIS)

Ericson, T.E.O.; Loiseau, B.; Rahm, J.; Blomgren, J.; Olsson, N.; Thomas, A. W.

1999-01-01

We report here a preliminary value for the πNN coupling constant deduced from the Goldberger-Miyazawa-Oehme sum rule for forward πN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives g 2 c =13.99(24)

Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling

Science.gov (United States)

Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.

2016-11-01

A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is

Systematics of gamma decay through low-lying vibrational levels of even--even nuclei excited by (p,p') and (n,n') reactions

International Nuclear Information System (INIS)

Koopman, R.P.

1977-01-01

A series of experiments was performed in which gamma-ray spectra were measured, using a Ge(Li) detector, for incident 7 to 26-MeV protons on the even-even vibrational nuclei 56 Fe, 62 Ni, 64 Zn, 108 Pd, 110 Cd, 114 Cd, 116 Cd, 116 Sn, 120 Sn, and 206 Pb, and for incident 14-MeV neutrons on natural Fe, Ni, Zn, Cd, Sn, and Pb. These measurements yielded gamma-ray cross sections from which it was inferred that almost all of the gamma cascades from (p,p') and (n,n') reactions passed down through the first 2 + levels. Consequently, the strength of the 2 + → 0 + gamma transitions were found to be an indirect measure of the (p,p') or (n,n') cross sections. Several types of nuclear model calculations were performed and compared with experimental results. These calculations included coupled-channel calculations to reproduce the direct, collective excitation of the low-lying levels, and statistical plus pre-equilibrium model calculations to reproduce the (p,p') and the (n,n') cross sections for comparison with the 2 + → 0 + gamma measurements. The agreement between calculation and experiment was generally good except at high energies, where pre-equilibrium processes dominate (i.e. around 26-MeV). Here discrepancies between calculations from the two different pre-equilibrium models and between the data and the calculations were found. Significant isospin mixing of T/sub greater than/ into T/sub less than/ states was necessary in order to have the calculations match the data for the (p,p') reactions, up to about 18-MeV

Hybrid time/frequency domain modeling of nonlinear components

DEFF Research Database (Denmark)

Wiechowski, Wojciech Tomasz; Lykkegaard, Jan; Bak, Claus Leth

2007-01-01

This paper presents a novel, three-phase hybrid time/frequency methodology for modelling of nonlinear components. The algorithm has been implemented in the DIgSILENT PowerFactory software using the DIgSILENT Programming Language (DPL), as a part of the work described in [1]. Modified HVDC benchmark...

Unambiguous amplitude analysis of NN {yields} {Delta}N transition from asymmetry measurements

Energy Technology Data Exchange (ETDEWEB)

Auger, J.P. [Universite d`Orleans (France). Lab. de Physique Theorique; Lazard, C. [Paris-11 Univ., 91 - Orsay (France). Div. de Physique Theorique

1997-12-31

For particular {Delta}-production angles, an unambiguous determination of the NN {yields} {Delta}N transition amplitudes is performed, from NN {yields} (N{pi})N experiments, in which the polarization states are measured in the entrance channel, only. A three-step method is developed, which determines, firstly, the magnitudes of the amplitudes, secondly, independent relative phases, and thirdly, some dependent relative phases for resolving the remaining discrete ambiguities. A rule of ambiguity elimination is applied, which is based on the closure of a chain of consecutive independent relative phases, by means of the ad-hoc dependent one. A generalization of this rule is given, for the case of a non-diagonal matrix connecting observables and bilinear combinations of amplitudes. (author) 18 refs.

Evidence of tensor correlations in the nuclear many-body system using a modern NN potential

International Nuclear Information System (INIS)

Fiase, J.O.; Nkoma, J.S.; Sharmaand, L.K.; Hosaka, A.

2003-01-01

In this paper we show evidence of the importance of tensor correlations in the nuclear many-body system by calculating the effective two-body nuclear matrix elements in the frame work of the Lowest-Order Constrained Variational (LOCV) technique with two-body correlation functions using the Reid93 potential. We have achieved this by switching on and off the strength of the tensor correlations, α k . We have found that in order to obtain reasonable agreement with earlier calculations based on the G-matrix theory, we must turn on the strength of the tensor correlations especially in the triplet even (TE) and tensor even (TNE) channels to take the value of approximately, 0.05. As an application, we have estimated the value of the Landau - Migdal parameter, g' NN which we found to be g' NN = 0.65. This compares favorably with the G-matrix calculated value of g' NN = 0.54. (author)

NN wave function at small distances and hard bremsstrahlung in the process pp→ppγ

International Nuclear Information System (INIS)

Neudatchin, V. G.; Khokhlov, N. A.; Shirokov, A. M.; Knyr, V. A.

1997-01-01

Various possibilities of studying the NN wave function at small distances--and in particular, quark degrees of freedom in the NN system--are discussed. It is shown that there is such a possibility at moderate energies--namely, hard bremsstrahlung in the process pp→ppγ at proton-beam energies in the range 350-450 MeV permits distinguishing between the pp wave function with nodes in S and P waves that corresponds to the Moscow potential of NN interaction from functions obtained with repulsive-core mesonic potentials. In the regions where photon energies in the c.m.s. are maximal (forward and backward photon emission angles in the laboratory frame), the pp→ppγ cross section calculated with the Moscow potential has maxima at which it is approximately five times larger than the analogous cross section calculated with repulsive-core mesonic potentials. The coordinate-representation formalism of the theory of bremsstrahlung is expounded

Non-Poisson counting statistics of a hybrid G-M counter dead time model

International Nuclear Information System (INIS)

Lee, Sang Hoon; Jae, Moosung; Gardner, Robin P.

2007-01-01

The counting statistics of a G-M counter with a considerable dead time event rate deviates from Poisson statistics. Important characteristics such as observed counting rates as a function true counting rates, variances and interval distributions were analyzed for three dead time models, non-paralyzable, paralyzable and hybrid, with the help of GMSIM, a Monte Carlo dead time effect simulator. The simulation results showed good agreements with the models in observed counting rates and variances. It was found through GMSIM simulations that the interval distribution for the hybrid model showed three distinctive regions, a complete cutoff region for the duration of the total dead time, a degraded exponential and an enhanced exponential regions. By measuring the cutoff and the duration of degraded exponential from the pulse interval distribution, it is possible to evaluate the two dead times in the hybrid model

Coupled thermal model of photovoltaic-thermoelectric hybrid panel for sample cities in Europe

DEFF Research Database (Denmark)

Rezaniakolaei, Alireza; Sera, Dezso; Rosendahl, Lasse Aistrup

2016-01-01

of the hybrid system under different weather conditions. The model takes into account solar irradiation, wind speed and ambient temperature as well as convective and radiated heat losses from the front and rear surfaces of the panel. The model is developed for three sample cities in Europe with different......In general, modeling of photovoltaic-thermoelectric (PV/TEG) hybrid panels have been mostly simplified and disconnected from the actual ambient conditions and thermal losses from the panel. In this study, a thermally coupled model of PV/TEG panel is established to precisely predict performance...... weather conditions. The results show that radiated heat loss from the front surface and the convective heat loss due to the wind speed are the most critical parameters on performance of the hybrid panel performance. The results also indicate that, with existing thermoelectric materials, the power...

Attenuation correction for hybrid MR/PET scanners: a comparison study

Energy Technology Data Exchange (ETDEWEB)

Rota Kops, Elena [Forschungszentrum Jülich GmbH, Jülich (Germany); Ribeiro, Andre Santos [Imperial College London, London (United Kingdom); Caldeira, Liliana [Forschungszentrum Jülich GmbH, Jülich (Germany); Hautzel, Hubertus [Heinrich-Heine-University Düsseldorf, Düsseldorf (Germany); Lukas, Mathias [Technische Universitaet Muenchen, Munich (Germany); Antoch, Gerald [Heinrich-Heine-University Düsseldorf, Düsseldorf (Germany); Lerche, Christoph; Shah, Jon [Forschungszentrum Jülich GmbH, Jülich (Germany)

2015-05-18

Attenuation correction of PET data acquired in hybrid MR/PET scanners is still a challenge. Different methods have been adopted by several groups to obtain reliable attenuation maps (mu-maps). In this study we compare three methods: MGH, UCL, Neural-Network. The MGH method is based on an MR/CT template obtained with the SPM8 software. The UCL method uses a database of MR/CT pairs. Both generate mu-maps from MP-RAGE images. The feed-forward neural-network from Juelich (NN-Juelich) requires two UTE images; it generates segmented mu-maps. Data from eight subjects (S1-S8) measured in the Siemens 3T MR-BrainPET scanner were used. Corresponding CT images were acquired. The resulting mu-maps were compared against the CT-based mu-maps for each subject and method. Overlapped voxels and Dice similarity coefficients, D, for bone, soft-tissue and air regions, and relative differences images were calculated. The true positive (TP) recognized voxels for the whole head were 79.9% (NN-Juelich, S7) to 92.1% (UCL method, S1). D values of the bone were D=0.65 (NN-Juelich, S1) to D=0.87 (UCL method, S1). For S8 the MHG method failed (TP=76.4%; D=0.46 for bone). D values shared a common tendency in all subjects and methods to recognize soft-tissue as bone. The relative difference images showed a variation of -10.9% - +10.1%; for S8 and MHG method the values were -24.5% and +14.2%. A preliminary comparison of three methods for generation of mu-maps for MR/PET scanners is presented. The continuous methods (MGH, UCL) seem to generate reliable mu-maps, whilst the binary method seems to need further improvement. Future work will include more subjects, the reconstruction of corresponding PET data and their comparison.

Attenuation correction for hybrid MR/PET scanners: a comparison study

International Nuclear Information System (INIS)

Rota Kops, Elena; Ribeiro, Andre Santos; Caldeira, Liliana; Hautzel, Hubertus; Lukas, Mathias; Antoch, Gerald; Lerche, Christoph; Shah, Jon

2015-01-01

Attenuation correction of PET data acquired in hybrid MR/PET scanners is still a challenge. Different methods have been adopted by several groups to obtain reliable attenuation maps (mu-maps). In this study we compare three methods: MGH, UCL, Neural-Network. The MGH method is based on an MR/CT template obtained with the SPM8 software. The UCL method uses a database of MR/CT pairs. Both generate mu-maps from MP-RAGE images. The feed-forward neural-network from Juelich (NN-Juelich) requires two UTE images; it generates segmented mu-maps. Data from eight subjects (S1-S8) measured in the Siemens 3T MR-BrainPET scanner were used. Corresponding CT images were acquired. The resulting mu-maps were compared against the CT-based mu-maps for each subject and method. Overlapped voxels and Dice similarity coefficients, D, for bone, soft-tissue and air regions, and relative differences images were calculated. The true positive (TP) recognized voxels for the whole head were 79.9% (NN-Juelich, S7) to 92.1% (UCL method, S1). D values of the bone were D=0.65 (NN-Juelich, S1) to D=0.87 (UCL method, S1). For S8 the MHG method failed (TP=76.4%; D=0.46 for bone). D values shared a common tendency in all subjects and methods to recognize soft-tissue as bone. The relative difference images showed a variation of -10.9% - +10.1%; for S8 and MHG method the values were -24.5% and +14.2%. A preliminary comparison of three methods for generation of mu-maps for MR/PET scanners is presented. The continuous methods (MGH, UCL) seem to generate reliable mu-maps, whilst the binary method seems to need further improvement. Future work will include more subjects, the reconstruction of corresponding PET data and their comparison.

Measurements of directed, elliptic, and triangular flow in Cu + Au collisions at √{sNN}=200 GeV

Science.gov (United States)

Adare, A.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Alexander, J.; Alfred, M.; Aoki, K.; Apadula, N.; 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.; 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.; Danley, T. W.; Datta, A.; Daugherity, M. S.; David, G.; Deblasio, K.; Dehmelt, K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Ding, L.; Dion, A.; Diss, P. B.; Do, J. H.; D'Orazio, L.; Drapier, O.; Drees, A.; Drees, K. A.; Durham, J. M.; Durum, A.; Engelmore, T.; Enokizono, A.; 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.; Fusayasu, T.; 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.; Hamilton, H. F.; Han, S. Y.; Hanks, J.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; Hayano, R.; He, X.; Hemmick, T. K.; Hester, T.; Hill, J. C.; Hollis, R. S.; Homma, K.; Hong, B.; Hoshino, T.; Hotvedt, N.; 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, 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.; Kijima, K. M.; Kim, C.; Kim, D. J.; Kim, E.-J.; Kim, G. W.; Kim, M.; Kim, Y.-J.; Kim, Y. K.; Kimelman, B.; Kistenev, E.; Kitamura, R.; 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.; 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.; Milov, A.; Mishra, D. K.; Mitchell, J. T.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Mohapatra, S.; Montuenga, P.; Moon, T.; Morrison, D. P.; Moskowitz, M.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Mwai, A.; Nagae, T.; Nagamiya, S.; Nagashima, K.; 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.; Nishimura, S.; Nouicer, R.; Novák, T.; Novitzky, N.; Nyanin, A. S.; O'Brien, E.; Ogilvie, C. A.; Oide, H.; Okada, K.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, J. S.; 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.; Ramson, B. J.; Ravinovich, I.; Read, K. F.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Rinn, T.; 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.; Snowball, M.; Solano, S.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; 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.; Tanaka, Y.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tennant, E.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Tomášek, M.; Torii, H.; Towell, C. L.; 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.; White, A. S.; Wolin, S.; Woody, C. L.; Wysocki, M.; Xia, B.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yanovich, A.; Yokkaichi, S.; Yoo, J. H.; Yoon, I.; You, Z.; Younus, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zelenski, A.; Zhou, S.; Zou, L.; Phenix Collaboration

2016-11-01

Measurements of anisotropic flow Fourier coefficients (vn) for inclusive charged particles and identified hadrons π±, K±, p , and p ¯ produced at midrapidity in Cu +Au collisions at √{s NN}=200 GeV are presented. The data were collected in 2012 by the PHENIX experiment at the Relativistic Heavy-Ion Collider (RHIC). The particle azimuthal distributions with respect to different-order symmetry planes Ψn, for n =1 , 2, and 3 are studied as a function of transverse momentum pT over a broad range of collision centralities. Mass ordering, as expected from hydrodynamic flow, is observed for all three harmonics. The charged-particle results are compared with hydrodynamical and transport model calculations. We also compare these Cu +Au results with those in Cu +Cu and Au +Au collisions at the same √{sNN} and find that the v2 and v3, as a function of transverse momentum, follow a common scaling with 1 /(ɛnNpart1 /3) .

Bayesian inference for hybrid discrete-continuous stochastic kinetic models

International Nuclear Information System (INIS)

Sherlock, Chris; Golightly, Andrew; Gillespie, Colin S

2014-01-01

We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process (MJP), computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either ‘fast’ or ‘slow’ with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a MJP with time-dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model. (paper)

Performance Modeling of Hybrid MPI/OpenMP Scientific Applications on Large-scale Multicore Cluster Systems

KAUST Repository

Wu, Xingfu; Taylor, Valerie

2011-01-01

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore clusters: IBM POWER4, POWER5+ and Blue Gene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore clusters because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyro kinetic Toroidal Code in magnetic fusion to validate our performance model of the hybrid application on these multicore clusters. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore clusters. © 2011 IEEE.

Performance Modeling of Hybrid MPI/OpenMP Scientific Applications on Large-scale Multicore Cluster Systems

KAUST Repository

Wu, Xingfu

2011-08-01

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore clusters: IBM POWER4, POWER5+ and Blue Gene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore clusters because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyro kinetic Toroidal Code in magnetic fusion to validate our performance model of the hybrid application on these multicore clusters. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore clusters. © 2011 IEEE.

HYBRID WAYS OF DOING: A MODEL FOR TEACHING PUBLIC SPACE

Directory of Open Access Journals (Sweden)

Gabrielle Bendiner-Viani

2010-07-01

Full Text Available This paper addresses an exploratory practice undertaken by the authors in a co-taught class to hybridize theory, research and practice. This experiment in critical transdisciplinary design education took the form of a “critical studio + practice-based seminar on public space”, two interlinked classes co-taught by landscape architect Elliott Maltby and environmental psychologist Gabrielle Bendiner-Viani at the Parsons, The New School for Design. This design process was grounded in the political and social context of the contested East River waterfront of New York City and valued both intensive study (using a range of social science and design methods and a partnership with a local community organization, engaging with the politics, issues and human needs of a complex site. The paper considers how we encouraged interdisciplinary collaboration and dialogue between teachers as well as between liberal arts and design students and developed strategies to overcome preconceived notions of traditional “studio” and “seminar” work. By exploring the challenges and adjustments made during the semester and the process of teaching this class, this paper addresses how we moved from a model of intertwining theory, research and practice, to a hybrid model of multiple ways of doing, a model particularly apt for teaching public space. Through examples developed for and during our course, the paper suggests practical ways of supporting this transdisciplinary hybrid model.

Hybrid Modeling Improves Health and Performance Monitoring

Science.gov (United States)

2007-01-01

Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.

The influence of nonlocal hybridization on ground-state properties of the Falicov-Kimball model

International Nuclear Information System (INIS)

Farkasovsky, Pavol

2005-01-01

The density matrix renormalization group is used to examine effects of nonlocal hybridization on ground-state properties of the Falicov-Kimball model (FKM) in one dimension. Special attention is devoted to the problem of hybridization-induced insulator-metal transition. It is shown that the picture of insulator-metal transitions found for the FKM with nonlocal hybridization strongly differs from one found for the FKM without hybridization (as well as with local hybridization). The effect of nonlocal hybridization is so strong that it can induce the insulator-metal transition, even in the half-filled band case where the ground states of the FKM without hybridization are insulating for all finite Coulomb interactions. Outside the half-filled band case the metal-insulator transition driven by pressure is found for finite values of nonlocal hybridization

Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

Science.gov (United States)

Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

2016-02-01

The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica

Directory of Open Access Journals (Sweden)

Proß Sabrina

2011-03-01

Full Text Available Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process.

Thermal modeling of a hydraulic hybrid vehicle transmission based on thermodynamic analysis

International Nuclear Information System (INIS)

Kwon, Hyukjoon; Sprengel, Michael; Ivantysynova, Monika

2016-01-01

Hybrid vehicles have become a popular alternative to conventional powertrain architectures by offering improved fuel efficiency along with a range of environmental benefits. Hydraulic Hybrid Vehicles (HHV) offer one approach to hybridization with many benefits over competing technologies. Among these benefits are lower component costs, more environmentally friendly construction materials, and the ability to recover a greater quantity of energy during regenerative braking which make HHVs partially well suited to urban environments. In order to further the knowledge base regarding HHVs, this paper explores the thermodynamic characteristics of such a system. A system model is detailed for both the hydraulic and thermal components of a closed circuit hydraulic hybrid transmission following the FTP-72 driving cycle. Among the new techniques proposed in this paper is a novel method for capturing rapid thermal transients. This paper concludes by comparing the results of this model with experimental data gathered on a Hardware-in-the-Loop (HIL) transmission dynamometer possessing the same architecture, components, and driving cycle used within the simulation model. This approach can be used for several applications such as thermal stability analysis of HHVs, optimal thermal management, and analysis of the system's thermodynamic efficiency. - Highlights: • Thermal modeling for HHVs is introduced. • A model for the hydraulic and thermal system is developed for HHVs. • A novel method for capturing rapid thermal transients is proposed. • The thermodynamic system diagram of a series HHV is predicted.

Direct determinations of the πNN coupling constants

International Nuclear Information System (INIS)

Ericson, T.E.O.; ); Loiseau, B.

1998-01-01

A novel extrapolation method has been used to deduce directly the charged πNN coupling constant from backward np differential scattering cross sections. The extracted value, g c 2 = 14.52(026)is higher than the indirectly deduced values obtained in nucleon-nucleon energy-dependent partial-wave analyses. Our preliminary direct value from a reanalysis of the GMO sum-rule points to an intermediate value of g c 2 about 13.97(30). (author)

Open Charm Yields in d+Au Collisions at √sNN = 200 GeV

International Nuclear Information System (INIS)

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.; Bravar, A.; Bystersky, M.; Cadman, R.V.; Cai, X.Z.; Caines, H.; Calderon de la Barca Sanchez, 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 Mazumda, 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.F.; 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.; Heppelmann, 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.; Ivanshin, Yu.I.; 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, S.M.; Kotchenda, L.; Kramer, M.; Kravtsov, P.; Kravtsov, V.I.; Krueger, K.; Kuhn, C.; Kulikov, A.I.; Kumar, A.

2005-01-01

Mid-rapidity open charm spectra from direct reconstruction of D 0 ((bar D) 0 ) → K ± π ± in d+Au collisions and indirect electron/positron measurements via charm semileptonic decays in p+p and d+Au collisions at √s NN = 200 GeV are reported. The D 0 ((bar D) 0 ) spectrum covers a transverse momentum (p T ) range of 0.1 T T cbar c 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

An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy

Directory of Open Access Journals (Sweden)

Ming-Chang Wu

2015-10-01

Full Text Available Floods, one of the most significant natural hazards, often result in loss of life and property. Accurate hourly streamflow forecasting is always a key issue in hydrology for flood hazard mitigation. To improve the performance of hourly streamflow forecasting, a methodology concerning the development of neural network (NN based models with an enforced learning strategy is proposed in this paper. Firstly, four different NNs, namely back propagation network (BPN, radial basis function network (RBFN, self-organizing map (SOM, and support vector machine (SVM, are used to construct streamflow forecasting models. Through the cross-validation test, NN-based models with superior performance in streamflow forecasting are detected. Then, an enforced learning strategy is developed to further improve the performance of the superior NN-based models, i.e., SOM and SVM in this study. Finally, the proposed flow forecasting model is obtained. Actual applications are conducted to demonstrate the potential of the proposed model. Moreover, comparison between the NN-based models with and without the enforced learning strategy is performed to evaluate the effect of the enforced learning strategy on model performance. The results indicate that the NN-based models with the enforced learning strategy indeed improve the accuracy of hourly streamflow forecasting. Hence, the presented methodology is expected to be helpful for developing improved NN-based streamflow forecasting models.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

Science.gov (United States)

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

Model-based design approaches for plug-in hybrid vehicle design

Energy Technology Data Exchange (ETDEWEB)

Mendes, C.J. [CrossChasm Technologies, Cambridge, ON (Canada); Stevens, M.B.; Fowler, M.W. [Waterloo Univ., ON (Canada). Dept. of Chemical Engineering; Fraser, R.A. [Waterloo Univ., ON (Canada). Dept. of Mechanical Engineering; Wilhelm, E.J. [Paul Scherrer Inst., Villigen (Switzerland). Energy Systems Analysis

2007-07-01

A model-based design process for plug-in hybrid vehicles (PHEVs) was presented. The paper discussed steps between the initial design concept and a working vehicle prototype, and focused on an investigation of the software-in-the-loop (SIL), hardware-in-the-loop (HIL), and component-in-the-loop (CIL) design phases. The role and benefits of using simulation were also reviewed. A method for mapping and identifying components was provided along with a hybrid control strategy and component-level control optimization process. The role of simulation in component evaluation, architecture design, and de-bugging procedures was discussed, as well as the role simulation networks can play in speeding deployment times. The simulations focused on work performed on a 2005 Chevrolet Equinox converted to a fuel cell hybrid electric vehicle (FCHEV). Components were aggregated to create a complete virtual vehicle. A simplified vehicle model was implemented onto the on-board vehicle control hardware. Optimization metrics were estimated at 10 alpha values during each control loop iteration. The simulation was then used to tune the control system under a variety of drive cycles and conditions. A CIL technique was used to place a physical hybrid electric vehicle (HEV) component under the control of a real time HEV/PHEV simulation. It was concluded that controllers should have a standardized component description that supports integration into advanced testing procedures. 4 refs., 9 figs.

Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier

Directory of Open Access Journals (Sweden)

Elize A. Shirdel

2011-01-01

Full Text Available Background. Delivery of full doses of adjuvant chemotherapy on schedule is key to optimal breast cancer outcomes. Neutropenia is a serious complication of chemotherapy and a common barrier to this goal, leading to dose reductions or delays in treatment. While past research has observed correlations between complete blood count data and neutropenic events, a reliable method of classifying breast cancer patients into low- and high-risk groups remains elusive. Patients and Methods. Thirty-five patients receiving adjuvant chemotherapy for early-stage breast cancer under the care of a single oncologist are examined in this study. FOS-3NN stratifies patient risk based on complete blood count data after the first cycle of treatment. All classifications are independent of breast cancer subtype and clinical markers, with risk level determined by the kinetics of patient blood count response to the first cycle of treatment. Results. In an independent test set of patients unseen by FOS-3NN, 19 out of 21 patients were correctly classified (Fisher’s exact test probability P<0.00023 [2 tailed], Matthews’ correlation coefficient +0.83. Conclusions. We have developed a model that accurately predicts neutropenic events in a population treated with adjuvant chemotherapy in the first cycle of a 6-cycle treatment.

Improving Hybrid III injury assessment in steering wheel rim to chest impacts using responses from finite element Hybrid III and human body model.

Science.gov (United States)

Holmqvist, Kristian; Davidsson, Johan; Mendoza-Vazquez, Manuel; Rundberget, Peter; Svensson, Mats Y; Thorn, Stefan; Törnvall, Fredrik

2014-01-01

The main aim of this study was to improve the quality of injury risk assessments in steering wheel rim to chest impacts when using the Hybrid III crash test dummy in frontal heavy goods vehicle (HGV) collision tests. Correction factors for chest injury criteria were calculated as the model chest injury parameter ratios between finite element (FE) Hybrid III, evaluated in relevant load cases, and the Total Human Model for Safety (THUMS). This is proposed to be used to compensate Hybrid III measurements in crash tests where steering wheel rim to chest impacts occur. The study was conducted in an FE environment using an FE-Hybrid III model and the THUMS. Two impactor shapes were used, a circular hub and a long, thin horizontal bar. Chest impacts at velocities ranging from 3.0 to 6.0 m/s were simulated at 3 impact height levels. A ratio between FE-Hybrid III and THUMS chest injury parameters, maximum chest compression C max, and maximum viscous criterion VC max, were calculated for the different chest impact conditions to form a set of correction factors. The definition of the correction factor is based on the assumption that the response from a circular hub impact to the middle of the chest is well characterized and that injury risk measures are independent of impact height. The current limits for these chest injury criteria were used as a basis to develop correction factors that compensate for the limitations in biofidelity of the Hybrid III in steering wheel rim to chest impacts. The hub and bar impactors produced considerably higher C max and VC max responses in the THUMS compared to the FE-Hybrid III. The correction factor for the responses of the FE-Hybrid III showed that the criteria responses for the bar impactor were consistently overestimated. Ratios based on Hybrid III and THUMS responses provided correction factors for the Hybrid III responses ranging from 0.84 to 0.93. These factors can be used to estimate C max and VC max values when the Hybrid III is

Iterated interactions method. Realistic NN potential

International Nuclear Information System (INIS)

Gorbatov, A.M.; Skopich, V.L.; Kolganova, E.A.

1991-01-01

The method of iterated potential is tested in the case of realistic fermionic systems. As a base for comparison calculations of the 16 O system (using various versions of realistic NN potentials) by means of the angular potential-function method as well as operators of pairing correlation were used. The convergence of genealogical series is studied for the central Malfliet-Tjon potential. In addition the mathematical technique of microscopical calculations is improved: new equations for correlators in odd states are suggested and the technique of leading terms was applied for the first time to calculations of heavy p-shell nuclei in the basis of angular potential functions

Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

Directory of Open Access Journals (Sweden)

Bogdan Gliwa

2011-01-01

Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

Modeling of renewable hybrid energy sources

Directory of Open Access Journals (Sweden)

Dumitru Cristian Dragos

2009-12-01

Full Text Available Recent developments and trends in the electric power consumption indicate an increasing use of renewable energy. Renewable energy technologies offer the promise of clean, abundant energy gathered from self-renewing resources such as the sun, wind, earth and plants. Virtually all regions of the world have renewable resources of one type or another. By this point of view studies on renewable energies focuses more and more attention. The present paper intends to present different mathematical models related to different types of renewable energy sources such as: solar energy and wind energy. It is also presented the validation and adaptation of such models to hybrid systems working in geographical and meteorological conditions specific to central part of Transylvania region. The conclusions based on validation of such models are also shown.

Chiral 2π exchange at fourth order and peripheral NN scattering

International Nuclear Information System (INIS)

Entem, D.R.; Machleidt, R.

2002-01-01

We calculate the impact of the complete set of two-pion exchange contributions at chiral fourth order (also known as next-to-next-to-next-to-leading order) on peripheral partial waves of nucleon-nucleon scattering. Our calculations are based upon the analytical studies by Kaiser. It turns out that the contribution of fourth order is substantially smaller than the one of third order, indicating convergence of the chiral expansion. We compare the prediction from chiral pion exchange with the corresponding one from conventional meson theory as represented by the Bonn full model and find, in general, good agreement. Our calculations provide a sound basis for investigating the issue whether the low-energy constants determined from πN lead to reasonable predictions for NN

A hybrid model for the computationally-efficient simulation of the cerebellar granular layer

Directory of Open Access Journals (Sweden)

Anna eCattani

2016-04-01

Full Text Available The aim of the present paper is to efficiently describe the membrane potential dynamics of neural populations formed by species having a high density difference in specific brain areas. We propose a hybrid model whose main ingredients are a conductance-based model (ODE system and its continuous counterpart (PDE system obtained through a limit process in which the number of neurons confined in a bounded region of the brain tissue is sent to infinity. Specifically, in the discrete model, each cell is described by a set of time-dependent variables, whereas in the continuum model, cells are grouped into populations that are described by a set of continuous variables.Communications between populations, which translate into interactions among the discrete and the continuous models, are the essence of the hybrid model we present here. The cerebellum and cerebellum-like structures show in their granular layer a large difference in the relative density of neuronal species making them a natural testing ground for our hybrid model. By reconstructing the ensemble activity of the cerebellar granular layer network and by comparing our results to a more realistic computational network, we demonstrate that our description of the network activity, even though it is not biophysically detailed, is still capable of reproducing salient features of neural network dynamics. Our modeling approach yields a significant computational cost reduction by increasing the simulation speed at least $270$ times. The hybrid model reproduces interesting dynamics such as local microcircuit synchronization, traveling waves, center-surround and time-windowing.

Hybrid Computational Model for High-Altitude Aeroassist Vehicles, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — A hybrid continuum/noncontinuum computational model will be developed for analyzing the aerodynamics and heating on aeroassist vehicles. Unique features of this...

A Hybrid Model for Forecasting Sales in Turkish Paint Industry

Directory of Open Access Journals (Sweden)

Alp Ustundag

2009-12-01

Full Text Available Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI with multiple linear regression (MLR to predict product sales for the largest Turkish paint producer. In the hybrid model, three different AI methods, fuzzy rule-based system (FRBS, artificial neural network (ANN and adaptive neuro fuzzy network (ANFIS, are used and compared to each other. The results indicate that FRBS yields better forecasting accuracy in terms of root mean squared error (RMSE and mean absolute percentage error (MAPE.

Optimization of ultrasonic array inspections using an efficient hybrid model and real crack shapes

Energy Technology Data Exchange (ETDEWEB)

Felice, Maria V., E-mail: maria.felice@bristol.ac.uk [Department of Mechanical Engineering, University of Bristol, Bristol, U.K. and NDE Laboratory, Rolls-Royce plc., Bristol (United Kingdom); Velichko, Alexander, E-mail: p.wilcox@bristol.ac.uk; Wilcox, Paul D., E-mail: p.wilcox@bristol.ac.uk [Department of Mechanical Engineering, University of Bristol, Bristol (United Kingdom); Barden, Tim; Dunhill, Tony [NDE Laboratory, Rolls-Royce plc., Bristol (United Kingdom)

2015-03-31

Models which simulate the interaction of ultrasound with cracks can be used to optimize ultrasonic array inspections, but this approach can be time-consuming. To overcome this issue an efficient hybrid model is implemented which includes a finite element method that requires only a single layer of elements around the crack shape. Scattering Matrices are used to capture the scattering behavior of the individual cracks and a discussion on the angular degrees of freedom of elastodynamic scatterers is included. Real crack shapes are obtained from X-ray Computed Tomography images of cracked parts and these shapes are inputted into the hybrid model. The effect of using real crack shapes instead of straight notch shapes is demonstrated. An array optimization methodology which incorporates the hybrid model, an approximate single-scattering relative noise model and the real crack shapes is then described.

Exact Cross-Validation for kNN and applications to passive and active learning in classification

OpenAIRE

Célisse, Alain; Mary-Huard, Tristan

2011-01-01

In the binary classification framework, a closed form expression of the cross-validation Leave-p-Out (LpO) risk estimator for the k Nearest Neighbor algorithm (kNN) is derived. It is first used to study the LpO risk minimization strategy for choosing k in the passive learning setting. The impact of p on the choice of k and the LpO estimation of the risk are inferred. In the active learning setting, a procedure is proposed that selects new examples using a LpO committee of kNN classifiers. The...

Model for optimum design of standalone hybrid renewable energy ...

African Journals Online (AJOL)

An optimization model for the design of a hybrid renewable energy microgrid ... and increasing the rated power of the wind energy conversion system (WECS) or solar ... a 70% reduction in gas emissions and an 80% reduction in energy costs.

Modeling and performance analysis of a concentrated photovoltaic–thermoelectric hybrid power generation system

International Nuclear Information System (INIS)

Lamba, Ravita; Kaushik, S.C.

2016-01-01

Highlights: • Thermodynamic model of concentrated photovoltaic–thermoelectric system is analysed. • Thomson effect reduces the power output of PV, TE and hybrid PV–TEG system. • Effect of thermocouple number, irradiance, PV and TE current have been studied. • The optimum concentration ratio for maximum power output has been found out. • The overall efficiency and power output of hybrid PV–TEG system has been improved. - Abstract: In this study, a thermodynamic model for analysing the performance of a concentrated photovoltaic–thermoelectric generator (CPV–TEG) hybrid system including Thomson effect in conjunction with Seebeck, Joule and Fourier heat conduction effects has been developed and simulated in MATALB environment. The expressions for calculating the temperature of photovoltaic (PV) module, hot and cold sides of thermoelectric (TE) module are derived analytically as well. The effect of concentration ratio, number of thermocouples in TE module, solar irradiance, PV module current and TE module current on power output and efficiency of the PV, TEG and hybrid PV–TEG system have been studied. The optimum concentration ratio corresponding to maximum power output of the hybrid system has been found out. It has been observed that by considering Thomson effect in TEG module, the power output of the PV, TE and hybrid PV–TEG systems decreases and at C = 1 and 5, it reduces the power output of hybrid system by 0.7% and 4.78% respectively. The results of this study may provide basis for performance optimization of a practical irreversible CPV–TEG hybrid system.

Modeling Hybridization Kinetics of Gene Probes in a DNA Biochip Using FEMLAB

Science.gov (United States)

Munir, Ahsan; Waseem, Hassan; Williams, Maggie R.; Stedtfeld, Robert D.; Gulari, Erdogan; Tiedje, James M.; Hashsham, Syed A.

2017-01-01

Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool (FEMLAB; package included in COMSOL Multiphysics) to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs). Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip (R2 = 0.8131). PMID:28555058

Modeling Hybridization Kinetics of Gene Probes in a DNA Biochip Using FEMLAB

Directory of Open Access Journals (Sweden)

Ahsan Munir

2017-05-01

Full Text Available Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool (FEMLAB; package included in COMSOL Multiphysics to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs. Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip (R2 = 0.8131.

Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses

International Nuclear Information System (INIS)

Li, Liang; You, Sixiong; Yang, Chao; Yan, Bingjie; Song, Jian; Chen, Zheng

2016-01-01

Highlights: • The novel approximated global optimal energy management strategy has been proposed for hybrid powertrains. • Eight typical driving behaviors have been classified with K-means to deal with the multiplicative traffic conditions. • The stochastic driver models of different driving behaviors were established based on the Markov chains. • ECMS was used to modify the SMPC-based energy management strategy to improve its fuel economy. • The approximated global optimal energy management strategy for plug-in hybrid electric buses has been verified and analyzed. - Abstract: Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K-means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

Science.gov (United States)

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

From hybrid swarms to swarms of hybrids

Science.gov (United States)

Stohlgren, Thomas J.; Szalanski, Allen L; Gaskin, John F.; Young, Nicholas E.; West, Amanda; Jarnevich, Catherine S.; Tripodi, Amber

2014-01-01

Science has shown that the introgression or hybridization of modern humans (Homo sapiens) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis, was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica, showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.

Hybrid modeling approach to improve the forecasting capability for the gaseous radionuclide in a nuclear site

International Nuclear Information System (INIS)

Jeong, Hyojoon; Hwang, Wontae; Kim, Eunhan; Han, Moonhee

2012-01-01

Highlights: ► This study is to improve the reliability of air dispersion modeling. ► Tracer experiments assumed gaseous radionuclides were conducted at a nuclear site. ► The performance of a hybrid modeling combined ISC with ANFIS was investigated.. ► Hybrid modeling approach shows better performance rather than a single ISC model. - Abstract: Predicted air concentrations of radioactive materials are important for an environmental impact assessment for the public health. In this study, the performance of a hybrid modeling combined with the industrial source complex (ISC) model and an adaptive neuro-fuzzy inference system (ANFIS) for predicting tracer concentrations was investigated. Tracer dispersion experiments were performed to produce the field data assuming the accidental release of radioactive material. ANFIS was trained in order that the outputs of the ISC model are similar to the measured data. Judging from the higher correlation coefficients between the measured and the calculated ones, the hybrid modeling approach could be an appropriate technique for an improvement of the modeling capability to predict the air concentrations for radioactive materials.

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.

Science.gov (United States)

Chen, Yumiao; Yang, Zhongliang

2017-01-01

Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.

A Generalized Hybrid Multiscale Modeling Approach for Flow and Reactive Transport in Porous Media

Science.gov (United States)

Yang, X.; Meng, X.; Tang, Y. H.; Guo, Z.; Karniadakis, G. E.

2017-12-01

Using emerging understanding of biological and environmental processes at fundamental scales to advance predictions of the larger system behavior requires the development of multiscale approaches, and there is strong interest in coupling models at different scales together in a hybrid multiscale simulation framework. A limited number of hybrid multiscale simulation methods have been developed for subsurface applications, mostly using application-specific approaches for model coupling. The proposed generalized hybrid multiscale approach is designed with minimal intrusiveness to the at-scale simulators (pre-selected) and provides a set of lightweight C++ scripts to manage a complex multiscale workflow utilizing a concurrent coupling approach. The workflow includes at-scale simulators (using the lattice-Boltzmann method, LBM, at the pore and Darcy scale, respectively), scripts for boundary treatment (coupling and kriging), and a multiscale universal interface (MUI) for data exchange. The current study aims to apply the generalized hybrid multiscale modeling approach to couple pore- and Darcy-scale models for flow and mixing-controlled reaction with precipitation/dissolution in heterogeneous porous media. The model domain is packed heterogeneously that the mixing front geometry is more complex and not known a priori. To address those challenges, the generalized hybrid multiscale modeling approach is further developed to 1) adaptively define the locations of pore-scale subdomains, 2) provide a suite of physical boundary coupling schemes and 3) consider the dynamic change of the pore structures due to mineral precipitation/dissolution. The results are validated and evaluated by comparing with single-scale simulations in terms of velocities, reactive concentrations and computing cost.

Energy Efficiency Comparison between Hydraulic Hybrid and Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Jia-Shiun Chen

2015-05-01

Full Text Available Conventional vehicles tend to consume considerable amounts of fuel, which generates exhaust gases and environmental pollution during intermittent driving cycles. Therefore, prospective vehicle designs favor improved exhaust emissions and energy consumption without compromising vehicle performance. Although pure electric vehicles feature high performance and low pollution characteristics, their limitations are their short driving range and high battery costs. Hybrid electric vehicles (HEVs are comparatively environmentally friendly and energy efficient, but cost substantially more compared with conventional vehicles. Hydraulic hybrid vehicles (HHVs are mainly operated using engines, or using alternate combinations of engine and hydraulic power sources while vehicles accelerate. When the hydraulic system accumulator is depleted, the conventional engine reengages; concurrently, brake-regenerated power is recycled and reused by employing hydraulic motor–pump modules in circulation patterns to conserve fuel and recycle brake energy. This study adopted MATLAB Simulink to construct complete HHV and HEV models for backward simulations. New European Driving Cycles were used to determine the changes in fuel economy. The output of power components and the state-of-charge of energy could be retrieved. Varying power component models, energy storage component models, and series or parallel configurations were combined into seven different vehicle configurations: the conventional manual transmission vehicle, series hybrid electric vehicle, series hydraulic hybrid vehicle, parallel hybrid electric vehicle, parallel hydraulic hybrid vehicle, purely electric vehicle, and hydraulic-electric hybrid vehicle. The simulation results show that fuel consumption was 21.80% lower in the series hydraulic hybrid vehicle compared to the series hybrid electric vehicle; additionally, fuel consumption was 3.80% lower in the parallel hybrid electric vehicle compared to the

A general model of distant hybridization reveals the conditions for extinction in Atlantic salmon and brown trout.

Science.gov (United States)

Quilodrán, Claudio S; Currat, Mathias; Montoya-Burgos, Juan I

2014-01-01

Interspecific hybridization is common in nature but can be increased in frequency or even originated by human actions, such as species introduction or habitat modification, which may threaten species persistence. When hybridization occurs between distantly related species, referred to as "distant hybridization," the resulting hybrids are generally infertile or fertile but do not undergo chromosomal recombination during gametogenesis. Here, we present a model describing this frequent but poorly studied interspecific hybridization to assess its consequences on parental species and to anticipate the conditions under which they can reach extinction. Our general model fully incorporates three important processes: density-dependent competition, dominance/recessivity inheritance of traits and assortative mating. We demonstrate its use and flexibility by assessing population extinction risk between Atlantic salmon and brown trout in Norway, whose interbreeding has recently increased due to farmed fish releases into the wild. We identified the set of conditions under which hybridization may threaten salmonid species. Thanks to the flexibility of our model, we evaluated the effect of an additional risk factor, a parasitic disease, and showed that the cumulative effects dramatically increase the extinction risk. The consequences of distant hybridization are not genetically, but demographically mediated. Our general model is useful to better comprehend the evolution of such hybrid systems and we demonstrated its importance in the field of conservation biology to set up management recommendations when this increasingly frequent type of hybridization is in action.

A General Model of Distant Hybridization Reveals the Conditions for Extinction in Atlantic Salmon and Brown Trout

Science.gov (United States)

Quilodrán, Claudio S.; Currat, Mathias; Montoya-Burgos, Juan I.

2014-01-01

Interspecific hybridization is common in nature but can be increased in frequency or even originated by human actions, such as species introduction or habitat modification, which may threaten species persistence. When hybridization occurs between distantly related species, referred to as “distant hybridization,” the resulting hybrids are generally infertile or fertile but do not undergo chromosomal recombination during gametogenesis. Here, we present a model describing this frequent but poorly studied interspecific hybridization to assess its consequences on parental species and to anticipate the conditions under which they can reach extinction. Our general model fully incorporates three important processes: density-dependent competition, dominance/recessivity inheritance of traits and assortative mating. We demonstrate its use and flexibility by assessing population extinction risk between Atlantic salmon and brown trout in Norway, whose interbreeding has recently increased due to farmed fish releases into the wild. We identified the set of conditions under which hybridization may threaten salmonid species. Thanks to the flexibility of our model, we evaluated the effect of an additional risk factor, a parasitic disease, and showed that the cumulative effects dramatically increase the extinction risk. The consequences of distant hybridization are not genetically, but demographically mediated. Our general model is useful to better comprehend the evolution of such hybrid systems and we demonstrated its importance in the field of conservation biology to set up management recommendations when this increasingly frequent type of hybridization is in action. PMID:25003336

A general model of distant hybridization reveals the conditions for extinction in Atlantic salmon and brown trout.

Directory of Open Access Journals (Sweden)

Claudio S Quilodrán

Full Text Available Interspecific hybridization is common in nature but can be increased in frequency or even originated by human actions, such as species introduction or habitat modification, which may threaten species persistence. When hybridization occurs between distantly related species, referred to as "distant hybridization," the resulting hybrids are generally infertile or fertile but do not undergo chromosomal recombination during gametogenesis. Here, we present a model describing this frequent but poorly studied interspecific hybridization to assess its consequences on parental species and to anticipate the conditions under which they can reach extinction. Our general model fully incorporates three important processes: density-dependent competition, dominance/recessivity inheritance of traits and assortative mating. We demonstrate its use and flexibility by assessing population extinction risk between Atlantic salmon and brown trout in Norway, whose interbreeding has recently increased due to farmed fish releases into the wild. We identified the set of conditions under which hybridization may threaten salmonid species. Thanks to the flexibility of our model, we evaluated the effect of an additional risk factor, a parasitic disease, and showed that the cumulative effects dramatically increase the extinction risk. The consequences of distant hybridization are not genetically, but demographically mediated. Our general model is useful to better comprehend the evolution of such hybrid systems and we demonstrated its importance in the field of conservation biology to set up management recommendations when this increasingly frequent type of hybridization is in action.

Preliminary Physics Summary: Inclusive $\\Upsilon$ production in p-Pb collisions at $\\sqrt{s_{\\rm NN}} = 8.16$ TeV

CERN Document Server

2018-01-01

The inclusive $\\Upsilon$ production in p-Pb interactions at the centre-of-mass energy per nucleon-nucleon collision $\\sqrt{s_{\\rm NN}} = 8.16$ TeV is studied with the ALICE detector at the CERN LHC. The measurement has been performed reconstructing $\\Upsilon$(1S) and $\\Upsilon$(2S) mesons via their dimuon decay channel, in the centre-of-mass rapidities $2.03 < y_{\\rm cms} < 3.53$ and $-4.46 < y_{\\rm cms} < -2.96$, down to zero transverse momentum. Preliminary results on the inclusive $\\Upsilon$(1S) nuclear modification factors ($R_{\\rm pPb}$) as a function of rapidity, transverse momentum ($p_{\\rm T}$) and centrality of the collisions will be presented. The results will be compared with those obtained by ALICE in p-Pb collisions at $\\sqrt{s_{\\rm NN}} = 8.16$ TeV and with theoretical model calculations. The inclusive $\\Upsilon$(2S) $R_{\\rm pPb}$ will also be discussed and compared to the corresponding $\\Upsilon(1S)$ measurement.

Hybrid incompatibility arises in a sequence-based bioenergetic model of transcription factor binding.

Science.gov (United States)

Tulchinsky, Alexander Y; Johnson, Norman A; Watt, Ward B; Porter, Adam H

2014-11-01

Postzygotic isolation between incipient species results from the accumulation of incompatibilities that arise as a consequence of genetic divergence. When phenotypes are determined by regulatory interactions, hybrid incompatibility can evolve even as a consequence of parallel adaptation in parental populations because interacting genes can produce the same phenotype through incompatible allelic combinations. We explore the evolutionary conditions that promote and constrain hybrid incompatibility in regulatory networks using a bioenergetic model (combining thermodynamics and kinetics) of transcriptional regulation, considering the bioenergetic basis of molecular interactions between transcription factors (TFs) and their binding sites. The bioenergetic parameters consider the free energy of formation of the bond between the TF and its binding site and the availability of TFs in the intracellular environment. Together these determine fractional occupancy of the TF on the promoter site, the degree of subsequent gene expression and in diploids, and the degree of dominance among allelic interactions. This results in a sigmoid genotype-phenotype map and fitness landscape, with the details of the shape determining the degree of bioenergetic evolutionary constraint on hybrid incompatibility. Using individual-based simulations, we subjected two allopatric populations to parallel directional or stabilizing selection. Misregulation of hybrid gene expression occurred under either type of selection, although it evolved faster under directional selection. Under directional selection, the extent of hybrid incompatibility increased with the slope of the genotype-phenotype map near the derived parental expression level. Under stabilizing selection, hybrid incompatibility arose from compensatory mutations and was greater when the bioenergetic properties of the interaction caused the space of nearly neutral genotypes around the stable expression level to be wide. F2's showed higher

Centrality dependence of the charged particle multiplicity near midrapidity in Au+Au collisions at (sNN)=130 and 200 GeV

Science.gov (United States)

Back, B. B.; Ballintijn, M.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Bickley, A.; 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.; Heintzelman, G.; Henderson, C.; Hicks, D.; Hofman, D.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A.; 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.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2002-06-01

The PHOBOS experiment has measured the charged particle multiplicity at midrapidity in Au+Au collisions at (sNN)=200 GeV as a function of the collision centrality. Results on dNch/dη\\|\\|η\\|/2 are presented as a function of . As was found from similar data at (sNN)=130 GeV, the data can be equally well described by parton saturation models and two-component fits, which include contributions that scale as Npart and the number of binary collisions Ncoll. We compare the data at the two energies by means of the ratio R200/130 of the charged particle multiplicity for the two different energies as a function of . For events with >100, we find that this ratio is consistent with a constant value of 1.14+/-0.01(stat)+/-0.05(syst).

Hybrid sediment transport model for the “linguado” channel, state of Santa Catarina, Brazil

Directory of Open Access Journals (Sweden)

Edison Conde Perez dos Santos

2017-12-01

Full Text Available This study involves an assessment of various artificial intelligence-related techniques which aim to produce a more robust system for sediment transport modeling. The intelligent systems developed in this research are directly applicable to academic knowledge and use data from a report on "water circulation assessment in the “Linguado” Channel and Babitonga Bay ,”Santa Catarina”, Brazil, developed by  Military Engineering Institute (IME. The solution employed for sediment transport was built using an intelligent system from the conception of two hybrid models. The first was a Neuro-Fuzzy (ANFIS hybrid model for the study of hydrodynamic behavior, aiming to determine flow rate in the channel. The second was a fuzzy genetic model, able to assess sediment transport in the “Linguado” Channel. The study's conclusion compares the different effects involved in the dredging equilibrium in the “Linguado” Channel according to this hybrid model with the results obtained using a finite element model in the MIKE21® software.

Model Predictive Control of the Hybrid Ventilation for Livestock

DEFF Research Database (Denmark)

Wu, Zhuang; Stoustrup, Jakob; Trangbæk, Klaus

2006-01-01

In this paper, design and simulation results of Model Predictive Control (MPC) strategy for livestock hybrid ventilation systems and associated indoor climate through variable valve openings and exhaust fans are presented. The design is based on thermal comfort parameters for poultry in barns...

Hybrid quantum-classical modeling of quantum dot devices

Science.gov (United States)

Kantner, Markus; Mittnenzweig, Markus; Koprucki, Thomas

2017-11-01

The design of electrically driven quantum dot devices for quantum optical applications asks for modeling approaches combining classical device physics with quantum mechanics. We connect the well-established fields of semiclassical semiconductor transport theory and the theory of open quantum systems to meet this requirement. By coupling the van Roosbroeck system with a quantum master equation in Lindblad form, we introduce a new hybrid quantum-classical modeling approach, which provides a comprehensive description of quantum dot devices on multiple scales: it enables the calculation of quantum optical figures of merit and the spatially resolved simulation of the current flow in realistic semiconductor device geometries in a unified way. We construct the interface between both theories in such a way, that the resulting hybrid system obeys the fundamental axioms of (non)equilibrium thermodynamics. We show that our approach guarantees the conservation of charge, consistency with the thermodynamic equilibrium and the second law of thermodynamics. The feasibility of the approach is demonstrated by numerical simulations of an electrically driven single-photon source based on a single quantum dot in the stationary and transient operation regime.

Axelrod Model of Social Influence with Cultural Hybridization

Science.gov (United States)

Radillo-Díaz, Alejandro; Pérez, Luis A.; Del Castillo-Mussot, Marcelo

2012-10-01

Since cultural interactions between a pair of social agents involve changes in both individuals, we present simulations of a new model based on Axelrod's homogenization mechanism that includes hybridization or mixture of the agents' features. In this new hybridization model, once a cultural feature of a pair of agents has been chosen for the interaction, the average of the values for this feature is reassigned as the new value for both agents after interaction. Moreover, a parameter representing social tolerance is implemented in order to quantify whether agents are similar enough to engage in interaction, as well as to determine whether they belong to the same cluster of similar agents after the system has reached the frozen state. The transitions from a homogeneous state to a fragmented one decrease in abruptness as tolerance is increased. Additionally, the entropy associated to the system presents a maximum within the transition, the width of which increases as tolerance does. Moreover, a plateau was found inside the transition for a low-tolerance system of agents with only two cultural features.

Non-linear hybrid control oriented modelling of a digital displacement machine

DEFF Research Database (Denmark)

Pedersen, Niels Henrik; Johansen, Per; Andersen, Torben O.

2017-01-01

Proper feedback control of digital fluid power machines (Pressure, flow, torque or speed control) requires a control oriented model, from where the system dynamics can be analyzed, stability can be proven and design criteria can be specified. The development of control oriented models for hydraulic...... Digital Displacement Machines (DDM) is complicated due to non-smooth machine behavior, where the dynamics comprises both analog, digital and non-linear elements. For a full stroke operated DDM the power throughput is altered in discrete levels based on the ratio of activated pressure chambers....... In this paper, a control oriented hybrid model is established, which combines the continuous non-linear pressure chamber dynamics and the discrete shaft position dependent activation of the pressure chambers. The hybrid machine model is further extended to describe the dynamics of a Digital Fluid Power...

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (n,1 Attrition Model

Directory of Open Access Journals (Sweden)

Xiangyong Chen

2014-01-01

hybrid dynamic systems is established based on Lanchester equation in a (n,1 battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation, and discrete event systems (described by variable tactics. Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1 case and solve the optimal strategies in a (2, 1 case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed.

A hybrid classical-quantum approach for ultra-scaled confined nanostructures : modeling and simulation*

Directory of Open Access Journals (Sweden)

Pietra Paola

2012-04-01

Full Text Available We propose a hybrid classical-quantum model to study the motion of electrons in ultra-scaled confined nanostructures. The transport of charged particles, considered as one dimensional, is described by a quantum effective mass model in the active zone coupled directly to a drift-diffusion problem in the rest of the device. We explain how this hybrid model takes into account the peculiarities due to the strong confinement and we present numerical simulations for a simplified carbon nanotube. Nous proposons un modèle hybride classique-quantique pour décrire le mouvement des électrons dans des nanostructures très fortement confinées. Le transport des particules, consideré unidimensionel, est décrit par un modèle quantique avec masse effective dans la zone active couplé à un problème de dérive-diffusion dans le reste du domaine. Nous expliquons comment ce modèle hybride prend en compte les spécificités de ce très fort confinement et nous présentons des résultats numériques pour un nanotube de carbone simplifié.

Hybrid quantum computation

International Nuclear Information System (INIS)

Sehrawat, Arun; Englert, Berthold-Georg; Zemann, Daniel

2011-01-01

We present a hybrid model of the unitary-evolution-based quantum computation model and the measurement-based quantum computation model. In the hybrid model, part of a quantum circuit is simulated by unitary evolution and the rest by measurements on star graph states, thereby combining the advantages of the two standard quantum computation models. In the hybrid model, a complicated unitary gate under simulation is decomposed in terms of a sequence of single-qubit operations, the controlled-z gates, and multiqubit rotations around the z axis. Every single-qubit and the controlled-z gate are realized by a respective unitary evolution, and every multiqubit rotation is executed by a single measurement on a required star graph state. The classical information processing in our model requires only an information flow vector and propagation matrices. We provide the implementation of multicontrol gates in the hybrid model. They are very useful for implementing Grover's search algorithm, which is studied as an illustrative example.

Renormalization of NN Interaction with Relativistic Chiral Two Pion Exchange

Energy Technology Data Exchange (ETDEWEB)

Higa, R; Valderrama, M Pavon; Arriola, E Ruiz

2007-06-14

The renormalization of the NN interaction with the Chiral Two Pion Exchange Potential computed using relativistic baryon chiral perturbation theory is considered. The short distance singularity reduces the number of counter-terms to about a half as those in the heavy-baryon expansion. Phase shifts and deuteron properties are evaluated and a general overall agreement is observed.

ΛΛ Correlation function in Au+Au collisions at √[S(NN)]=200  GeV.

Science.gov (United States)

Adamczyk, L; Adkins, J K; Agakishiev, G; Aggarwal, M M; Ahammed, Z; Alekseev, I; Alford, J; Anson, C D; Aparin, A; Arkhipkin, D; Aschenauer, E C; Averichev, G S; Banerjee, A; Beavis, D R; Bellwied, R; Bhasin, A; Bhati, A K; Bhattarai, P; Bichsel, H; Bielcik, J; Bielcikova, J; Bland, L C; Bordyuzhin, I G; Borowski, W; Bouchet, J; Brandin, A V; Brovko, S G; Bültmann, S; Bunzarov, I; Burton, T P; Butterworth, J; Caines, H; Calderón de la Barca Sánchez, M; Campbell, J M; Cebra, D; Cendejas, R; Cervantes, M C; Chaloupka, P; Chang, Z; Chattopadhyay, S; Chen, H F; Chen, J H; Chen, L; Cheng, J; Cherney, M; Chikanian, A; Christie, W; Chwastowski, J; Codrington, M J M; Contin, G; Cramer, J G; Crawford, H J; Cui, X; Das, S; Davila Leyva, A; De Silva, L C; Debbe, R R; Dedovich, T G; Deng, J; Derevschikov, A A; Derradi de Souza, R; di Ruzza, B; Didenko, L; Dilks, C; Ding, F; Djawotho, P; Dong, X; Drachenberg, J L; Draper, J E; Du, C M; Dunkelberger, L E; Dunlop, J C; Efimov, L G; Engelage, J; Engle, K S; Eppley, G; Eun, L; Evdokimov, O; Eyser, O; Fatemi, R; Fazio, S; Fedorisin, J; Filip, P; Fisyak, Y; Flores, C E; Gagliardi, C A; Gangadharan, D R; Garand, D; Geurts, F; Gibson, A; Girard, M; Gliske, S; Greiner, L; Grosnick, D; Gunarathne, D S; Guo, Y; Gupta, A; Gupta, S; Guryn, W; Haag, B; Hamed, A; Han, L-X; Haque, R; Harris, J W; Heppelmann, S; Hirsch, A; Hoffmann, G W; Hofman, D J; Horvat, S; Huang, B; Huang, H Z; Huang, X; Huck, P; Humanic, T J; Igo, G; Jacobs, W W; Jang, H; Judd, E G; Kabana, S; Kalinkin, D; Kang, K; Kauder, K; Ke, H W; Keane, D; Kechechyan, A; Kesich, A; Khan, Z H; Kikola, D P; Kisel, I; Kisiel, A; Koetke, D D; Kollegger, T; Konzer, J; Koralt, I; 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, C; Li, W; Li, X; Li, X; Li, Y; Li, Z M; Lisa, M A; Liu, F; Ljubicic, T; Llope, W J; Lomnitz, M; Longacre, R S; Luo, X; Ma, G L; Ma, Y G; Mahapatra, D P; Majka, R; Margetis, S; Markert, C; Masui, H; Matis, H S; McDonald, D; McShane, T S; Minaev, N G; Mioduszewski, S; Mohanty, B; Mondal, M M; Morozov, D A; Mustafa, M K; Nandi, B K; Nasim, Md; Nayak, T K; Nelson, J M; Nigmatkulov, G; Nogach, L V; Noh, S Y; Novak, J; Nurushev, S B; Odyniec, G; Ogawa, A; Oh, K; Ohlson, A; Okorokov, V; Oldag, E W; Olvitt, D L; Page, B S; Pan, Y X; Pandit, Y; Panebratsev, Y; Pawlak, T; Pawlik, B; Pei, H; Perkins, C; Pile, P; Planinic, M; Pluta, J; Poljak, N; Poniatowska, K; Porter, J; Poskanzer, A M; Pruthi, N K; Przybycien, M; Putschke, J; Qiu, H; Quintero, A; Ramachandran, S; Raniwala, R; Raniwala, S; Ray, R L; Riley, C K; Ritter, H G; Roberts, J B; Rogachevskiy, O V; Romero, J L; Ross, J F; Roy, A; Ruan, L; Rusnak, J; Rusnakova, O; Sahoo, N R; Sahu, P K; Sakrejda, I; Salur, S; Sandweiss, J; Sangaline, E; 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, B; Shen, W Q; Shi, S S; Shou, Q Y; Sichtermann, E P; Simko, M; Skoby, M J; Smirnov, D; Smirnov, N; Solanki, D; Sorensen, P; Spinka, H M; Srivastava, B; Stanislaus, T D S; Stevens, J R; Stock, R; Strikhanov, M; Stringfellow, B; Sumbera, M; Sun, X; Sun, X M; Sun, Y; Sun, Z; Surrow, B; Svirida, D N; Symons, T J M; Szelezniak, M A; Takahashi, J; Tang, A H; Tang, Z; Tarnowsky, T; Thomas, J H; Timmins, A R; Tlusty, D; Tokarev, M; Trentalange, S; Tribble, R E; Tribedy, P; Trzeciak, B A; Tsai, O D; Turnau, J; Ullrich, T; Underwood, D G; Van Buren, G; van Nieuwenhuizen, G; Vandenbroucke, M; Vanfossen, J A; Varma, R; Vasconcelos, G M S; Vasiliev, A N; Vertesi, R; Videbæk, F; Viyogi, Y P; Vokal, S; Vossen, A; Wada, M; Wang, F; Wang, G; Wang, H; Wang, J S; Wang, X L; Wang, Y; Wang, Y; Webb, G; Webb, J C; Westfall, G D; Wieman, H; Wissink, S W; Witt, R; Wu, Y F; Xiao, Z; Xie, W; Xin, K; Xu, H; Xu, J; Xu, N; Xu, Q H; Xu, Y; Xu, Z; Yan, W; Yang, C; Yang, Y; Yang, Y; Ye, Z; Yepes, P; Yi, L; Yip, K; Yoo, I-K; Yu, N; Zbroszczyk, H; Zha, W; Zhang, J B; Zhang, J L; Zhang, S; Zhang, X P; Zhang, Y; Zhang, Z P; Zhao, F; Zhao, J; Zhong, C; Zhu, X; Zhu, Y H; Zoulkarneeva, Y; Zyzak, M

2015-01-16

We present ΛΛ correlation measurements in heavy-ion collisions for Au+Au collisions at sqrt[s_{NN}]=200  GeV using the STAR experiment at the Relativistic Heavy-Ion Collider. 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 dihyperon searches are discussed.

A New Model for Baryogenesis through Hybrid Inflation

International Nuclear Information System (INIS)

Delepine, D.; Prieto, C. Martinez; Lopez, L. A. Urena

2009-01-01

We propose a hybrid inflation model with a complex waterfall field which contains an interaction term that breaks the U(1) global symmetry associated to the waterfall field charge. The asymmetric evolution of the real and imaginary parts of the complex field during the phase transition at the end of inflation translates into a charge asymmetry.

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

Science.gov (United States)

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 hybrid fuzzy multi-criteria decision making model for green ...

African Journals Online (AJOL)

A hybrid fuzzy multi-criteria decision making model for green supplier selection. ... Hence,supplier selection is significant factor in supply chain success. ... reduce purchasing cost, lead time and improve quality and environmental issue.

MODEL APLIKASI FIKIH MUAMALAH PADA FORMULASI HYBRID CONTRACT

Directory of Open Access Journals (Sweden)

Ali Murtadho

2013-10-01

Full Text Available Modern literatures of fiqh mu’āmalah talk alot about various contract formulation with capability of maximizing profit in shariah finance industry. This new contract modification is the synthesis among existing contracts which is formulated in such a way to be an integrated contract. This formulation is known as a hybrid contract or multicontract (al-'uqūd al-murakkabah. Some of them are, bay' bi thaman 'ājil, Ijārah muntahiyah bi ’l-tamlīk dan mushārakah mutanāqiṣah. This study intends to further describe models of hybrid contract, and explore the shari'ah principles in modern financial institutions. This study found a potential shift from the ideal values of the spirit of shari'ah into the spirit of competition based shari'ah formally.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems

Science.gov (United States)

Winkelmann, Stefanie; Schütte, Christof

2017-09-01

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems.

Science.gov (United States)

Winkelmann, Stefanie; Schütte, Christof

2017-09-21

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Software development infrastructure for the HYBRID modeling and simulation project

International Nuclear Information System (INIS)

Epiney, Aaron S.; Kinoshita, Robert A.; Kim, Jong Suk; Rabiti, Cristian; Greenwood, M. Scott

2016-01-01

One of the goals of the HYBRID modeling and simulation project is to assess the economic viability of hybrid systems in a market that contains renewable energy sources like wind. The idea is that it is possible for the nuclear plant to sell non-electric energy cushions, which absorb (at least partially) the volatility introduced by the renewable energy sources. This system is currently modeled in the Modelica programming language. To assess the economics of the system, an optimization procedure is trying to find the minimal cost of electricity production. The RAVEN code is used as a driver for the whole problem. It is assumed that at this stage, the HYBRID modeling and simulation framework can be classified as non-safety “research and development” software. The associated quality level is Quality Level 3 software. This imposes low requirements on quality control, testing and documentation. The quality level could change as the application development continues.Despite the low quality requirement level, a workflow for the HYBRID developers has been defined that include a coding standard and some documentation and testing requirements. The repository performs automated unit testing of contributed models. The automated testing is achieved via an open-source python script called BuildingsP from Lawrence Berkeley National Lab. BuildingsPy runs Modelica simulation tests using Dymola in an automated manner and generates and runs unit tests from Modelica scripts written by developers. In order to assure effective communication between the different national laboratories a biweekly videoconference has been set-up, where developers can report their progress and issues. In addition, periodic face-face meetings are organized intended to discuss high-level strategy decisions with management. A second means of communication is the developer email list. This is a list to which everybody can send emails that will be received by the collective of the developers and managers

Software development infrastructure for the HYBRID modeling and simulation project

Energy Technology Data Exchange (ETDEWEB)

Epiney, Aaron S. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kinoshita, Robert A. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kim, Jong Suk [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Greenwood, M. Scott [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2016-09-01

One of the goals of the HYBRID modeling and simulation project is to assess the economic viability of hybrid systems in a market that contains renewable energy sources like wind. The idea is that it is possible for the nuclear plant to sell non-electric energy cushions, which absorb (at least partially) the volatility introduced by the renewable energy sources. This system is currently modeled in the Modelica programming language. To assess the economics of the system, an optimization procedure is trying to find the minimal cost of electricity production. The RAVEN code is used as a driver for the whole problem. It is assumed that at this stage, the HYBRID modeling and simulation framework can be classified as non-safety “research and development” software. The associated quality level is Quality Level 3 software. This imposes low requirements on quality control, testing and documentation. The quality level could change as the application development continues.Despite the low quality requirement level, a workflow for the HYBRID developers has been defined that include a coding standard and some documentation and testing requirements. The repository performs automated unit testing of contributed models. The automated testing is achieved via an open-source python script called BuildingsP from Lawrence Berkeley National Lab. BuildingsPy runs Modelica simulation tests using Dymola in an automated manner and generates and runs unit tests from Modelica scripts written by developers. In order to assure effective communication between the different national laboratories a biweekly videoconference has been set-up, where developers can report their progress and issues. In addition, periodic face-face meetings are organized intended to discuss high-level strategy decisions with management. A second means of communication is the developer email list. This is a list to which everybody can send emails that will be received by the collective of the developers and managers

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

Science.gov (United States)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network

Modeling the geometric formation and powder deposition mass in laser induction hybrid cladding

International Nuclear Information System (INIS)

Huang, Yong Jun; Yuan, Sheng Fa

2012-01-01

A new laser induction hybrid cladding technique on cylinder work piece is presented. Based on a series of laser induction hybrid experiments by off axial powder feeding, the predicting models of individual clad geometric formation and powder catchment were developed in terms of powder feeding rate, laser special energy and induction energy density using multiple regression analysis. In addition, confirmation tests were performed to make a comparison between the predicting results and measured ones. Via the experiments and analysis, the conclusions can be lead to that the process parameters have crucial influence on the clad geometric formation and powder catchment, and that the predicting model reflects well the relationship between the clad geometric formation and process parameters in laser induction hybrid cladding

Hybrid modeling of microbial exopolysaccharide (EPS) production: The case of Enterobacter A47.

Science.gov (United States)

Marques, Rodolfo; von Stosch, Moritz; Portela, Rui M C; Torres, Cristiana A V; Antunes, Sílvia; Freitas, Filomena; Reis, Maria A M; Oliveira, Rui

2017-03-20

Enterobacter A47 is a bacterium that produces high amounts of a fucose-rich exopolysaccharide (EPS) from glycerol residue of the biodiesel industry. The fed-batch process is characterized by complex non-linear dynamics with highly viscous pseudo-plastic rheology due to the accumulation of EPS in the culture medium. In this paper, we study hybrid modeling as a methodology to increase the predictive power of models for EPS production optimization. We compare six hybrid structures that explore different levels of knowledge-based and machine-learning model components. Knowledge-based components consist of macroscopic material balances, Monod type kinetics, cardinal temperature and pH (CTP) dependency and power-law viscosity models. Unknown dependencies are set to be identified by a feedforward artificial neural network (ANN). A semiparametric identification schema is applied resorting to a data set of 13 independent fed-batch experiments. A parsimonious hybrid model was identified that describes the dynamics of the 13 experiments with the same parameterization. The final model is specific to Enterobacter A47 but can be easily extended to other microbial EPS processes. Copyright © 2017 Elsevier B.V. All rights reserved.

compounds with N=N, C≡C or conjugated double-bonded systems

Indian Academy of Sciences (India)

Unusual products in the reactions of phosphorus(III) compounds with. N=N, C≡C or conjugated double-bonded systems. K C KUMARA SWAMY,* E BALARAMAN, M PHANI PAVAN, N N BHUVAN KUMAR,. K PRAVEEN KUMAR and N SATISH KUMAR. School of Chemistry, University of Hyderabad, Hyderabad 500 046.

A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

Directory of Open Access Journals (Sweden)

Paweł Sitek

2016-01-01

Full Text Available This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP and constraint logic programming (CLP, were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems. The ECLiPSe system with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

Infectious disease modeling a hybrid system approach

CERN Document Server

Liu, Xinzhi

2017-01-01

This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Science.gov (United States)

Tong, Cao; Gong, Haili

2018-03-01

This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

Hierarchical modeling of molecular energies using a deep neural network

Science.gov (United States)

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.

A control-oriented simulation model of a power-split hybrid electric vehicle

International Nuclear Information System (INIS)

Cipek, Mihael; Pavković, Danijel; Petrić, Joško

2013-01-01

Highlights: ► A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed. ► Modeling the energy losses in the HEV transmission components are presented. ► The control optimization model implementation aspects are discussed. -- Abstract: A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed in this paper for the purpose of HEV dynamics analysis and control system design. The bond graph methodology is used to model dominant dynamic effects of the mechanical part of the HEV transmission. Simple quasi-static battery model, the environment model, the tire and the power losses model of a vehicle are included, as well. A low-level electric generator speed control loop is designed, which includes a PI controller tuned according to the symmetrical optimum tuning procedure. Finally, off-line optimization by conjugate gradient-based BPTT-like optimal control algorithm, which is based on the presented mathematical model, is also given in the paper.

Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

International Nuclear Information System (INIS)

Guerra, Fabio A.; Coelho, Leandro dos S.

2008-01-01

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

Anisotropic flow of charged particles in Pb-Pb collisions at $\\sqrt{s_{\\rm NN}}$ = 5.02 TeV

CERN Document Server

Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Shakeel; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Balasubramanian, Supraja; Baldisseri, Alberto; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Benacek, Pavel; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Bourjau, Christian; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Sanchez Gonzalez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Grachov, Oleg Anatolievich; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Gronefeld, Julius Maximilian; Grosse-Oetringhaus, Jan Fiete; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Harris, John William; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hillemanns, Hartmut; Hippolyte, Boris; Horak, David; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Inaba, Motoi; Incani, Elisa; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jahnke, Cristiane; Jakubowska, Monika Joanna; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karayan, Lilit; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Dong Jo; Kim, Daehyeok; Kim, Hyeonjoong; Kim, Jinsook; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kostarakis, Panagiotis; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kralik, Ivan; Kravcakova, Adela; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Ajay; Kumar, Jitendra; Lokesh, Kumar; Kumar, Shyam; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron De Guevara, Pedro; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Lutz, Tyler Harrison; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Davide; Pagano, Paola; Paic, Guy; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Scapparone, Eugenio; Scarlassara, Fernando; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shahzad, Muhammed Ikram; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Derradi De Souza, Rafael; Sozzi, Federica; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thakur, Dhananjaya; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trombetta, Giuseppe; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Weiser, Dennis Franz; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yasin, Zafar; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Chunhui, Zhang; Zhang, Zuman; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym

2016-04-01

We report the first results of elliptic ($v_2$), triangular ($v_3$) and quadrangular flow ($v_4$) of charged particles in Pb--Pb collisions at $\\sqrt{s_{_{\\rm NN}}}=$ 5.02 TeV with the ALICE detector at the CERN Large Hadron Collider. The measurements are performed in the central pseudorapidity region $|\\eta| <$ 0.8 and for the transverse momentum range 0.2 $< p_{\\rm T} < $ 5 GeV/$c$. The anisotropic flow is measured using two--particle correlations with a pseudorapidity gap greater than one unit and with the multi--particle cumulant method. Compared to results from Pb--Pb collisions at $\\sqrt{s_{_{\\rm NN}}}=$ 2.76 TeV, the anisotropic flow coefficients $v_{2}$, $v_{3}$ and $v_{4}$ are found to increase by (3.0$\\pm$0.6)\\%, (4.3$\\pm$1.4)\\% and (10.2$\\pm$3.8)\\%, respectively, over the centrality range 0-50\\%. This can be attributed mostly to an increase of the average transverse momentum between the two energies. The measurements are found to be compatible with hydrodynamic model calculations. This com...

Study on driver model for hybrid truck based on driving simulator experimental results

Directory of Open Access Journals (Sweden)

Dam Hoang Phuc

2018-04-01

Full Text Available In this paper, a proposed car-following driver model taking into account some features of both the compensatory and anticipatory model representing the human pedal operation has been verified by driving simulator experiments with several real drivers. The comparison between computer simulations performed by determined model parameters with the experimental results confirm the correctness of this mathematical driver model and identified model parameters. Then the driver model is joined to a hybrid vehicle dynamics model and the moderate car following maneuver simulations with various driver parameters are conducted to investigate influences of driver parameters on vehicle dynamics response and fuel economy. Finally, major driver parameters involved in the longitudinal control of drivers are clarified. Keywords: Driver model, Driver-vehicle closed-loop system, Car Following, Driving simulator/hybrid electric vehicle (B1

A Probability-Based Hybrid User Model for Recommendation System

Directory of Open Access Journals (Sweden)

Jia Hao

2016-01-01

Full Text Available With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

A modeling method of semiconductor fabrication flows with extended knowledge hybrid Petri nets

Institute of Scientific and Technical Information of China (English)

Zhou Binghai; Jiang Shuyu; Wang Shijin; Wu bin

2008-01-01

A modeling method of extended knowledge hybrid Petri nets (EKHPNs), incorporating object-oriented methods into hybrid Petri nets (HPNs), was presented and used for the representation and modeling of semiconductor wafer fabrication flows. To model the discrete and continuous parts of a complex semiconductor wafer fabrication flow, the HPNs were introduced into the EKHPNs. Object-oriented methods were combined into the EKHPNs for coping with the complexity of the fabrication flow. Knowledge annotations were introduced to solve input and output conflicts of the EKHPNs.Finally, to demonstrate the validity of the EKHPN method, a real semiconductor wafer fabrication case was used to illustrate the modeling procedure. The modeling results indicate that the proposed method can be used to model a complex semiconductor wafer fabrication flow expediently.

Higher harmonic flow coefficients of identified hadrons in Pb-Pb collisions at √sNN = 2.76 TeV

NARCIS (Netherlands)

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.; Albuquerque, D. S. D.; 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.; An, M.; Andrei, C.; Andrews, H. A.; 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.|info:eu-repo/dai/nl/411263188; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-moreno, E.; Beltran, L. G. 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.; 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.; Bonora, 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.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.|info:eu-repo/dai/nl/411885812; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; 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.; Crkovska, J.; 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.; De Souza, R. D.; Deisting, A.; Deloff, A.; Dénes, E.; 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.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; 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.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-solis, E.; Garg, K.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; 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.; Gonzalez, A. S.; Gonzalez, V.; González-zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.|info:eu-repo/dai/nl/326052577; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; 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.; 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.; 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.|info:eu-repo/dai/nl/370530780; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, J.; 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.|info:eu-repo/dai/nl/074064975; 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.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.|info:eu-repo/dai/nl/411295721; Lehner, S.; 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.|info:eu-repo/dai/nl/412461684; 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.; Milano, L.; Milosevic, J.; Mischke, A.|info:eu-repo/dai/nl/325781435; Mishra, A. N.; Mishra, T.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.|info:eu-repo/dai/nl/369405870; 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.; 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.|info:eu-repo/dai/nl/07051349X; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Oleniacz, J.; Oliveira Da Silva, A. C.|info:eu-repo/dai/nl/323375618; 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, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.|info:eu-repo/dai/nl/304833959; 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.; Ravasenga, I.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.|info:eu-repo/dai/nl/413319628; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; 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.; 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.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, 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.|info:eu-repo/dai/nl/165585781; 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.; 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.; 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.; Valencia Palomo, L.; Van Der Maarel, J.|info:eu-repo/dai/nl/412860996; Van Hoorne, J. W.; van Leeuwen, Marco|info:eu-repo/dai/nl/250599171; 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.|info:eu-repo/dai/nl/413533751; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vickovic, L.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; 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.; Wagner, B.; Wagner, J.; Wang, H.|info:eu-repo/dai/nl/369509307; 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.; 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.

2016-01-01

The elliptic, triangular, quadrangular and pentagonal anisotropic flow coefficients for π±, K± and p+p ¯ ¯ ¯ p+p¯ in Pb-Pb collisions at s NN − − − √ =2.76 sNN=2.76 TeV were measured with the ALICE detector at the Large Hadron Collider. The results were obtained with the Scalar Product method,

Evaluation of normalization methods for cDNA microarray data by k-NN classification

Energy Technology Data Exchange (ETDEWEB)

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J

2004-12-17

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

Helicity dependence of the γd→ πNN reactions in the Δ-resonance region

International Nuclear Information System (INIS)

Ahrens, J.; Arends, H.J.; Beck, R.; Heid, E.; Jahn, O.; Lang, M.; Martinez-Fabregate, M.; Schwamb, M.; Tamas, G.; Thomas, A.; Altieri, S.; Panzeri, A.; Pinelli, T.; Annand, J.R.M.; McGeorge, J.C.; Protopopescu, D.; Rosner, G.; Blackston, M.A.; Weller, H.R.; Bradtke, C.; Dutz, H.; Klein, F.; Rohlof, C.; Braghieri, A.; Pedroni, P.; Hose, N. d'; Fix, A.; Kondratiev, R.; Lisin, V.; Meyer, W.; Reicherz, G.; Rostomyan, T.; Ryckbosch, D.

2010-01-01

The helicity dependence of the differential cross-section for the γd→πNN reactions has been measured for the first time in the Δ -resonance region. The measurement was performed with the large-acceptance detector DAPHNE at the tagged photon beam facility of the MAMI accelerator in Mainz. The data show that the main reaction mechanisms for the π ± NN channels are the quasi-free N π processes on one bound nucleon with nuclear dynamics playing a minor role. On the contrary, for the π 0 np channel nuclear mechanisms involving the reabsorption of the photoproduced π 0 by the np pair have to be taken into account to reproduce the experimental data. (orig.)

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (n,1) Attrition Model

OpenAIRE

Chen, Xiangyong; Zhang, Ancai

2014-01-01

For the particularity of warfare hybrid dynamic process, a class of warfare hybrid dynamic systems is established based on Lanchester equation in a (n,1) battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation), and discrete event systems (described by variable tactics). Furthermore, an expository discussion is presented on an optimal variable tact...

Quark compound bag (QCB) model and nucleon-nucleon interaction

International Nuclear Information System (INIS)

Simonov, Yu.A.

1983-01-01

Quark degrees of freedom are treated in the NN system in the framework of the QCB model. The resulting QCB potential is in agreement with experimental data. P-matrix analysis inherent to the QCB model is discussed in detail. Applications of the QCB model are given including the weak NN interaction

Chiral approach to nuclear matter: Role of explicit short-range NN-terms

International Nuclear Information System (INIS)

Fritsch, S.; Kaiser, N.

2004-01-01

We extend a recent chiral approach to nuclear matter by including the most general (momentum-independent) NN-contact interaction. Iterating this two-parameter contact vertex with itself and with one-pion exchange the emerging energy per particle exhausts all terms possible up to and including fourth order in the small momentum expansion. Two (isospin-dependent) cut-offs Λ 0,1 are introduced to regularize the (linear) divergences of some three-loop in-medium diagrams. The equation of state of pure neutron matter, anti E n (k n ), can be reproduced very well up to quite high neutron densities of ρ n =0.5 fm -3 by adjusting the strength of a repulsive nn-contact interaction. Binding and saturation of isospin-symmetric nuclear matter is a generic feature of our perturbative calculation. Fixing the maximum binding energy per particle to - anti E(k f0 )=15.3 MeV we find that any possible equilibrium density ρ 0 lies below ρ 0 max =0.191 fm -3 . The additional constraint from the neutron matter equation of state leads however to a somewhat too low saturation density of ρ 0 =0.134 fm -3 . We also investigate the effects of the NN-contact interaction on the complex single-particle potential U(p,k f )+iW(p,k f ). We find that the effective nucleon mass at the Fermi surface is bounded from below by M * (k f0 ) ≥1.4 M. This property keeps the critical temperature of the liquid-gas phase transition at somewhat too high values T c ≥21 MeV. The downward bending of the asymmetry energy A(k f ) above nuclear-matter saturation density is a generic feature of the approximation to fourth order. We furthermore investigate the effects of the NN-contact interaction on the (vector-∇ρ) 2 -term in the nuclear energy density functional E[ρ,τ]. Altogether, there is within this complete fourth-order calculation no ''magic'' set of adjustable short-range parameters with which one could reproduce simultaneously and accurately all semi-empirical properties of nuclear matter. In

New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

DEFF Research Database (Denmark)

Dong, Yiqiu; Zeng, Tieyong

2013-01-01

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness...

Centrality dependence of pion freeze-out radii in Pb-Pb collisions at $\\sqrt{\\mathbf{s_{NN}}}$=2.76 TeV

CERN Document Server

Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahn, Sang Un; Aimo, Ilaria; Aiola, Salvatore; Ajaz, Muhammad; Akindinov, Alexander; Alam, Sk Noor; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Armesto Perez, Nestor; Arnaldi, Roberta; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Baldisseri, Alberto; Baltasar Dos Santos Pedrosa, Fernando; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blanco, Fernando; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Boettger, Stefan; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Cavicchioli, Costanza; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Zhang, Chunhui; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; D'Erasmo, Ginevra; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Dobrowolski, Tadeusz Antoni; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Engel, Heiko; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Eschweiler, Dominic; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Felea, Daniel; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Grosse-Oetringhaus, Jan Fiete; Grossiord, Jean-Yves; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gulkanyan, Hrant; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hansen, Alexander; Harris, John William; Hartmann, Helvi; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Heide, Markus Ansgar; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hilden, Timo Eero; Hillemanns, Hartmut; Hippolyte, Boris; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Huang, Meidana; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Ilkiv, Iryna; Inaba, Motoi; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacobs, Peter Martin; Jadlovska, Slavka; Jahnke, Cristiane; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jung, Hyungtaik; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karayan, Lilit; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Kamal; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Beomkyu; Kim, Do Won; Kim, Dong Jo; Kim, Hyeonjoong; Kim, Jinsook; Kim, Mimae; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobayashi, Taiyo; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kravcakova, Adela; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kugathasan, Thanushan; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kulakov, Igor; Kumar, Ajay; Kumar, Jitendra; Lokesh, Kumar; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kushpil, Svetlana; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Legrand, Iosif; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Ferreira Natal Da Luz, Pedro Hugo; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Mastroserio, Annalisa; Masui, Hiroshi; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Minervini, Lazzaro Manlio; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Morando, Maurizio; Moreira De Godoy, Denise Aparecida; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Murray, Sean; Musa, Luciano; Musinsky, Jan; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Nattrass, Christine; Nayak, Kishora; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Paola; Paic, Guy; Pajares Vales, Carlos; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Pant, Divyash; Papcun, Peter; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Real, Jean-Sebastien; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rivetti, Angelo; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Romita, Rosa; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salgado Lopez, Carlos Alberto; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schuster, Tim Robin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Seo, Jeewon; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Shigaki, Kenta; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Soegaard, Carsten; Soltz, Ron Ariel; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stefanek, Grzegorz; Steinpreis, Matthew Donald; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Sultanov, Rishat; Sumbera, Michal; Symons, Timothy; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tangaro, Marco-Antonio; Tapia Takaki, Daniel Jesus; Tarantola Peloni, Attilio; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Wang, Yifei; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Wessels, Johannes Peter; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yaldo, Chris G; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym

2016-02-04

We report on the measurement of freeze-out radii for pairs of identical-charge pions measured in Pb--Pb collisions at $\\sqrt{s_{\\rm NN}}=2.76$ TeV as a function of collision centrality and the average transverse momentum of the pair $k_{\\rm T}$. Three-dimensional sizes of the system (femtoscopic radii), as well as direction-averaged one-dimensional radii are extracted. The radii decrease with $k_{\\rm T}$, following a power-law behavior. This is qualitatively consistent with expectations from a collectively expanding system, produced in hydrodynamic calculations. The radii also scale linearly with $\\left^{1/3}$. This behaviour is compared to world data on femtoscopic radii in heavy-ion collisions. While the dependence is qualitatively similar to results at smaller $\\sqrt{s_{\\rm NN}}$, a decrease in the $R_{\\rm out}/R_{\\rm side}$ ratio is seen, which is in qualitative agreement with specific predictions from hydrodynamic models. The results provide further evidence for the production of a collective, strongly c...

Country Selection Model for Sustainable Construction Businesses Using Hybrid of Objective and Subjective Information

Directory of Open Access Journals (Sweden)

Kang-Wook Lee

2017-05-01

Full Text Available An important issue for international businesses and academia is selecting countries in which to expand in order to achieve entrepreneurial sustainability. This study develops a country selection model for sustainable construction businesses using both objective and subjective information. The objective information consists of 14 variables related to country risk and project performance in 32 countries over 25 years. This hybrid model applies subjective weighting from industrial experts to objective information using a fuzzy LinPreRa-based Analytic Hierarchy Process. The hybrid model yields a more accurate country selection compared to a purely objective information-based model in experienced countries. Interestingly, the hybrid model provides some different predictions with only subjective opinions in unexperienced countries, which implies that expert opinion is not always reliable. In addition, feedback from five experts in top international companies is used to validate the model’s completeness, effectiveness, generality, and applicability. The model is expected to aid decision makers in selecting better candidate countries that lead to sustainable business success.

Modelling of combined ICRF and NBI heating in JET hybrid plasmas

Directory of Open Access Journals (Sweden)

Gallart Dani

2017-01-01

Full Text Available During the 2015-2016 JET campaigns many efforts have been devoted to the exploration of high performance plasma scenarios envisaged for ITER operation. In this paper we model the combined ICRF+NBI heating in selected key hybrid discharges using PION. The antenna frequency was tuned to match the cyclotron frequency of minority hydrogen (H at the center of the tokamak coinciding with the second harmonic cyclotron resonance of deuterium. The modelling takes into account the synergy between ICRF and NBI heating through the second harmonic cyclotron resonance of deuterium beam ions which allows us to assess its impact on the neutron rate RNT. We evaluate the influence of H concentration which was varied in different discharges in order to test their role in the heating performance. According to our modelling, the ICRF enhancement of RNT increases by decreasing the H concentration which increases the ICRF power absorbed by deuterons. We find that in the recent hybrid discharges this ICRF enhancement was in the range of 10-25%. Finally, we extrapolate the results to D-T and find that the best performing hybrid discharges correspond to an equivalent fusion power of ∼7.0 MW in D-T.

New Models of Hybrid Leadership in Global Higher Education

Science.gov (United States)

Tonini, Donna C.; Burbules, Nicholas C.; Gunsalus, C. K.

2016-01-01

This manuscript highlights the development of a leadership preparation program known as the Nanyang Technological University Leadership Academy (NTULA), exploring the leadership challenges unique to a university undergoing rapid growth in a highly multicultural context, and the hybrid model of leadership it developed in response to globalization.…

A hybrid modeling approach for option pricing

Science.gov (United States)

Hajizadeh, Ehsan; Seifi, Abbas

2011-11-01

The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.

Hybrid Model for e-Learning Quality Evaluation

Directory of Open Access Journals (Sweden)

Suzana M. Savic

2012-02-01

Full Text Available E-learning is becoming increasingly important for the competitive advantage of economic organizations and higher education institutions. Therefore, it is becoming a significant aspect of quality which has to be integrated into the management system of every organization or institution. The paper examines e-learning quality characteristics, standards, criteria and indicators and presents a multi-criteria hybrid model for e-learning quality evaluation based on the method of Analytic Hierarchy Process, trend analysis, and data comparison.

Azimuthal anisotropy in Au+Au collisions at √sNN = 200 GeV

International Nuclear Information System (INIS)

Adams, J.; Aggarwal, M.M.; Ahammed, Z.; Amonett, J.; Anderson, B.D.; Akhipkin, 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.; Calderon de la Barca Sanchez, M.; Carroll, J.; Castillo, J.; Cebra, D.; Chajecki, Z.; Chaloupka, P.; Chattopdhyay, 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.; Foley, K.J.; Fomenko, K.; Fu, J.; Gagliardi, C.A.; 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.; Klyachko, A.; Koetke, D.D.; Kollegger, T.; Kopytine, M.; Kotchenda, L.; Kramer, M.; Kravtsov, P.; Kravtsov, V.I.; Krueger, K.; Kuhn, C.; Kulikov, A.I.

2004-01-01

The results from the STAR Collaboration on directed flow (v 1 ), elliptic flow (v 2 ), and the fourth harmonic (v 4 ) in the anisotropic azimuthal distribution of particles from Au+Au collisions at √s NN = 200 GeV are summarized and compared with results from other experiments and theoretical models. Results for identified particles are presented and fit with a Blast Wave model. For v 2 , scaling with the number of constituent quarks and parton coalescence is discussed. For v 4 , scaling with v 22 and quark coalescence predictions for higher harmonic flow is discussed. The different anisotropic flow analysis methods are compared and nonflow effects are extracted from the data. For v 2 , scaling with the number of constituent quarks and parton coalescence are discussed. For v 2 2 and quark coalescence are discussed

HYBRID CONTINUUM-DISCONTINUUM MODELLING OF ROCK FRACUTRE PROCESS IN BRAZILIAN TENSILE STRENGTH TEST

Directory of Open Access Journals (Sweden)

Huaming An

2017-10-01

Full Text Available A hybrid continuum-discontinuum method is introduced to model the rock failure process in Brazilian tensile strength (BTS test. The key component of the hybrid continuum-discontinuum method, i.e. transition from continuum to discontinuum through fracture and fragmentation, is introduced in detail. A laboratory test is conducted first to capture the rock fracture pattern in the BTS test while the tensile strength is calculated according to the peak value of the loading forces. Then the proposed method is used to model the rock behaviour during BTS test. The stress propagation is modelled and compared with those modelled by finite element method in literatures. In addition, the crack initiation and propagation are captured and compared with the facture patter in laboratory test. Moreover, the force-loading displacement curve is obtained which represents a typical brittle material failure process. Furthermore, the stress distributions along the vertical direction are compared with the theoretical solution. It is concluded that the hybrid continuum-discontinuum method can model the stress propagation process and the entire rock failure process in BTS test. The proposed method is a valuable numerical tool for studying the rock behaviour involving the fracture and fragmentation processes.

The electrochemical selective reduction of NO using CoSe2@CNTs hybrid.

Science.gov (United States)

Liu, Hui; Xiang, Kaisong; Yang, Bentao; Xie, Xiaofeng; Wang, Dongli; Zhang, Cong; Liu, Zhilou; Yang, Shu; Liu, Cao; Zou, Jianping; Chai, Liyuan

2017-06-01

Converting the NO from gaseous pollutant into NH 4 + through electrocatalytical reduction using cost-effective materials holds great promise for pollutant purifying and resources recycling. In this work, we developed a highly selective and stable catalyst CoSe 2 nanoparticle hybridized with carbon nanotubes (CoSe 2 @CNTs). The CoSe 2 @CNTs hybrid catalysts performed an extraordinary high selectivity for NH 4 + formation in NO electroreduction with minimal N 2 O production and H 2 evolution. The specific spatial structure of CoSe 2 is conductive to the predominant formation of N-H bond between the N from adsorbed NO and H and inhibition of N-N formation from adjacent adsorbed NO. It was also the first time to convert the coordinated NO into NH 4 + using non-noble metal catalysis. Moreover, the original concept of employing CoSe 2 as eletrocatalyst for NO hydrogenation presented in this work can broaden horizons and provide new dimensions in the design of new highly efficient catalysts for NH 4 + synthesis in aqueous solution.

Image Restoration Based on the Hybrid Total-Variation-Type Model

OpenAIRE

Shi, Baoli; Pang, Zhi-Feng; Yang, Yu-Fei

2012-01-01

We propose a hybrid total-variation-type model for the image restoration problem based on combining advantages of the ROF model with the LLT model. Since two ${L}^{1}$ -norm terms in the proposed model make it difficultly solved by using some classically numerical methods directly, we first employ the alternating direction method of multipliers (ADMM) to solve a general form of the proposed model. Then, based on the ADMM and the Moreau-Yosida decomposition theory, a more efficient method call...

Accuracy improvement of a hybrid robot for ITER application using POE modeling method

International Nuclear Information System (INIS)

Wang, Yongbo; Wu, Huapeng; Handroos, Heikki

2013-01-01