Fictitious fluxes in doped antiferromagnets

In a tight binding model of charged spin-1/2 electrons on a square lattice, a fully polarized ferromagnetic spin configuration generates an apparent U(1) flux given by $2\pi$ times the skyrmion charge density of the ferromagnetic order parameter. We show here that for an antiferromagnet, there are two ``fictitious'' magnetic fields, one staggered and one unstaggered. The staggered topological flux per unit cell can be varied between $-\pi\le\Phi\le\pi$ with a negligible change in the value of the effective nearest neighbor coupling constant whereas the magnitude of the unstaggered flux is strongly coupled to the magnitude of the second neighbor effective coupling.Comment: RevTeX, 5 pages including 4 figure

Semigroups in symmetric Lie groups

AbstractLet G be a Lie group and L C G a Lie subgroup. We give necessary and sufficient conditions for a family of cosets of L to generate a subsemigroup with nonempty interior in G. We apply these conditions to symmetric pairs (G, L) where L is a subgroup of G such that Go C L C Gi and r is an involutive automorphism of G. As a consequence we prove that for several r the fixed point group GI is a maximal semigroup

Extending the study of the Higgs sector at the LHC by proton tagging

We show that forward proton tagging may significantly enlarge the potential of studying the Higgs sector at the LHC. We concentrate on Higgs production via central exclusive diffractive processes (CEDP). Particular attention is paid to regions in the MSSM parameter space where the partial width of the Higgs boson decay into two gluons much exceeds the SM case; here the CEDP are found to have special advantages

Harnessing the hybrid power supply systems of utility grid and photovoltaic panels at retrofit residential single family building in Medan

The paper describes improvisation mode of energy supply source by collaboration between national utility grid as represented by fossil fuels and PV as independent renewable power resource in order to aim the energy consumptions efficiently in retrofit single family house. In this case, one existing single family house model in Medan, Indonesia was observed for the possibility of future refurbishment. The eco-design version of the house model and prediction analyses regarding nearby potential renewable energy resource (solar system) had been made using Autodesk Revit MEP 2015, Climate Consultant 6.0 and Green Building Studio Analysis. Economical evaluation of using hybrid power supply is discussed as well. © Published under licence by IOP Publishing Ltd

A reduced basis ensemble Kalman method

In the process of reproducing the state dynamics of parameter dependent distributed systems, data from physical measurements can be incorporated into the mathematical model to reduce the parameter uncertainty and, consequently, improve the state prediction. Such a data assimilation process must deal with the data and model misfit arising from experimental noise as well as model inaccuracies and uncertainties. In this work, we focus on the ensemble Kalman method (EnKM), a particle-based iterative regularization method designed for a posteriori analysis of time series. The method is gradient free and, like the ensemble Kalman filter (EnKF), relies on a sample of parameters or particle ensemble to identify the state that better reproduces the physical observations, while preserving the physics of the system as described by the best knowledge model. We consider systems described by parameterized parabolic partial differential equations and employ model order reduction techniques to generate surrogate models of different accuracy with uncertain parameters. Their use in combination with the EnKM involves the introduction of the model bias which constitutes a new source of systematic error. To mitigate its impact, an algorithm adjustment is proposed accounting for a prior estimation of the bias in the data. The resulting RB-EnKM is tested in different conditions, including different ensemble sizes and increasing levels of experimental noise. The results are compared to those obtained with the standard EnKF and with the unadjusted algorithm.</p

A reduced basis ensemble Kalman method

In the process of reproducing the state dynamics of parameter dependent distributed systems, data from physical measurements can be incorporated into the mathematical model to reduce the parameter uncertainty and, consequently, improve the state prediction. Such a data assimilation process must deal with the data and model misfit arising from experimental noise as well as model inaccuracies and uncertainties. In this work, we focus on the ensemble Kalman method (EnKM), a particle-based iterative regularization method designed for a posteriori analysis of time series. The method is gradient free and, like the ensemble Kalman filter (EnKF), relies on a sample of parameters or particle ensemble to identify the state that better reproduces the physical observations, while preserving the physics of the system as described by the best knowledge model. We consider systems described by parameterized parabolic partial differential equations and employ model order reduction techniques to generate surrogate models of different accuracy with uncertain parameters. Their use in combination with the EnKM involves the introduction of the model bias which constitutes a new source of systematic error. To mitigate its impact, an algorithm adjustment is proposed accounting for a prior estimation of the bias in the data. The resulting RB-EnKM is tested in different conditions, including different ensemble sizes and increasing levels of experimental noise. The results are compared to those obtained with the standard EnKF and with the unadjusted algorithm.</p

Central Exclusive Scalar Luminosities from the Linked Dipole Chain Model gluon densities

We investigate the implication of uncertainties in the unintegrated gluon distribution for the predictions for central exclusive production of scalars at hadron colliders. We use parameterizations of the kT-unintegrated gluon density obtained from the Linked Dipole Chain model, applying different options for the treatment of non-leading terms. We find that the luminosity function for central exclusive production is very sensitive to details of the transverse momentum distribution of the gluon which, contrary to the kT-integrated distribution, is not very well constrained experimentally

Prototyping to Leverage Learning in Product Manufacturing Environments

AbstractRooted in the automotive industry, this article discusses the topic of leveraging tacit knowledge through prototyping. After first providing an overview on learning and knowledge, the Socialization, Externalization, Combination and Internalization (SECI) model is discussed in detail, with a clear distinction between tacit and explicit knowledge. Based on this model, we propose a framework for using said reflective and affirmative prototyping in an external vs. internal learning/knowledge capturing and transfer setting. Contextual examples from select automotive manufacturing R&D projects are given to demonstrate the importance and potential in applying more effective strategies for knowledge transformation in engineering design