Dielectrics in a time-dependent electric field: a real-time approach based on density-polarization functional theory

In the presence of a (time-dependent) macroscopic electric field the electron dynamics of dielectrics cannot be described by the time-dependent density only. We present a real-time formalism that has the density and the macroscopic polarization P as key quantities. We show that a simple local function of P already captures long-range correlation in linear and nonlinear optical response functions. Specifically, after detailing the numerical implementation, we examine the optical absorption, the second- and third-harmonic generation of bulk Si, GaAs, AlAs and CdTe at different level of approximation. We highlight links with ultranonlocal exchange-correlation functional approximations proposed within linear response time-dependent density functional theory framework.

An empirical polarization domain channel availability model for cognitive radio

In dynamic spectrum access networks, cognitive radio terminals monitor their spectral environment in order to detect and opportunistically access unoccupied frequency channels. The overall performance of such networks depends on the spectrum occupancy or availability patterns. Accurate knowledge on the channel availability enables optimum performance of such networks in terms of spectrum and energy efficiency. This work proposes a novel probabilistic channel availability model that can describe the channel availability in different polarizations for mobile cognitive radio terminals that are likely to change their orientation during their operation. A Gaussian approximation is used to model the empirical occupancy data that was obtained through a measurement campaign in the cellular frequency bands within a realistic operational scenario.

Type Ia supernovae from violent mergers of carbon-oxygen white dwarfs: polarization signatures

The violent merger of two carbon-oxygen white dwarfs has been proposed as a viable progenitor for some Type Ia supernovae. However, it has been argued that the strong ejecta asymmetries produced by this model might be inconsistent with the low degree of polarization typically observed in Type Ia supernova explosions. Here, we test this claim by carrying out a spectropolarimetric analysis for the model proposed by Pakmor et al. for an explosion triggered during the merger of a 1.1 and 0.9 M⊙ carbon-oxygen white dwarf binary system. Owing to the asymmetries of the ejecta, the polarization signal varies significantly with viewing angle. We find that polarization levels for observers in the equatorial plane are modest (≲1 per cent) and show clear evidence for a dominant axis, as a consequence of the ejecta symmetry about the orbital plane. In contrast, orientations out of the plane are associated with higher degrees of polarization and departures from a dominant axis. While the particular model studied here gives a good match to highly polarized events such as SN 2004dt, it has difficulties in reproducing the low polarization levels commonly observed in normal Type Ia supernovae. Specifically, we find that significant asymmetries in the element distribution result in a wealth of strong polarization features that are not observed in the majority of currently available spectropolarimetric data of Type Ia supernovae. Future studies will map out the parameter space of the merger scenario to investigate if alternative models can provide better agreement with observations.

Btk regulates macrophage polarization in response to lipopolysaccharide

Bacterial Lipopolysaccharide (LPS) is a strong inducer of inflammation and does so by inducing polarization of macrophages to the classic inflammatory M1 population. Given the role of Btk as a critical signal transducer downstream of TLR4, we investigated its role in M1/M2 induction. In Btk deficient (Btk (-\-)) mice we observed markedly reduced recruitment of M1 macrophages following intraperitoneal administration of LPS. Ex vivo analysis demonstrated an impaired ability of Btk(-/-) macrophages to polarize into M1 macrophages, instead showing enhanced induction of immunosuppressive M2-associated markers in response to M1 polarizing stimuli, a finding accompanied by reduced phosphorylation of STAT1 and enhanced STAT6 phosphorylation. In addition to STAT activation, M1 and M2 polarizing signals modulate the expression of inflammatory genes via differential activation of transcription factors and regulatory proteins, including NF-κB and SHIP1. In keeping with a critical role for Btk in macrophage polarization, we observed reduced levels of NF-κB p65 and Akt phosphorylation, as well as reduced induction of the M1 associated marker iNOS in Btk(-/-) macrophages in response to M1 polarizing stimuli. Additionally enhanced expression of SHIP1, a key negative regulator of macrophage polarisation, was observed in Btk(-/-) macrophages in response to M2 polarizing stimuli. Employing classic models of allergic M2 inflammation, treatment of Btk (-/-) mice with either Schistosoma mansoni eggs or chitin resulted in increased recruitment of M2 macrophages and induction of M2-associated genes. This demonstrates an enhanced M2 skew in the absence of Btk, thus promoting the development of allergic inflammation.

Broadband Dual-Polarized Antenna with High Port Isolation and Polarization Purity

This paper presents a novel high symmetry balun which significantly improves the performance of dipole-based dual-polarized antennas. The new balun structure provides enhanced differential capability leading to high performance in terms of port-to-port isolation and far-field cross polarization. An example antenna using this balun is proposed. The simulated results show 53.5% of fractional bandwidth within the band 1.71−2.96 GHz (VSWR<1.5) and port-to-port isolation >59 dB. The radiation characteristic shows around 9 dBi of gain and far-field cross polarization <−48 dBi over the entire bandwidth. The detailed balun functioning and full antenna measurements will be presented during the conference. Performance comparison with similar structures will be also provided.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Noncollinear Polarization Gating of Attosecond Pulse Trains in the Relativistic Regime

High order harmonics generated at relativistic intensities have long been recognized as a route to the most powerful extreme ultraviolet pulses. Reliably generating isolated attosecond pulses requires gating to only a single dominant optical cycle, but techniques developed for lower power lasers have not been readily transferable. We present a novel method to temporally gate attosecond pulse trains by combining noncollinear and polarization gating. This scheme uses a split beam configuration which allows pulse gating to be implemented at the high beam fluence typical of multi-TW to PW class laser systems. Scalings for the gate width demonstrate that isolated attosecond pulses are possible even for modest pulse durations achievable for existing and planned future ultrashort high-power laser systems. Experimental results demonstrating the spectral effects of temporal gating on harmonic spectra generated by a relativistic laser plasma interaction are shown.

Predicting polarization signatures for double-detonation and delayed-detonation models of Type Ia supernovae

Calculations of synthetic spectropolarimetry are one means to test multidimensional explosion models for Type Ia supernovae. In a recent paper, we demonstrated that the violent merger of a 1.1 and 0.9 M⊙ white dwarf binary system is too asymmetric to explain the low polarization levels commonly observed in normal Type Ia supernovae. Here, we present polarization simulations for two alternative scenarios: the sub-Chandrasekhar mass double-detonation and the Chandrasekhar mass delayed-detonation model. Specifically, we study a 2D double-detonation model and a 3D delayed-detonation model, and calculate polarization spectra for multiple observer orientations in both cases. We find modest polarization levels (<1 per cent) for both explosion models. Polarization in the continuum peaks at ∼0.1–0.3 per cent and decreases after maximum light, in excellent agreement with spectropolarimetric data of normal Type Ia supernovae. Higher degrees of polarization are found across individual spectral lines. In particular, the synthetic Si II λ6355 profiles are polarized at levels that match remarkably well the values observed in normal Type Ia supernovae, while the low degrees of polarization predicted across the O I λ7774 region are consistent with the non-detection of this feature in current data. We conclude that our models can reproduce many of the characteristics of both flux and polarization spectra for well-studied Type Ia supernovae, such as SN 2001el and SN 2012fr. However, the two models considered here cannot account for the unusually high level of polarization observed in extreme cases such as SN 2004dt.

Towards optical polarization control of laser-driven proton acceleration in foils undergoing relativistic transparency

Control of the collective response of plasma particles to intense laser light is intrinsic to relativistic optics, the development of compact laser-driven particle and radiation sources, as well as investigations of some laboratory astrophysics phenomena. We recently demonstrated that a relativistic plasma aperture produced in an ultra-thin foil at the focus of intense laser radiation can induce diffraction, enabling polarization-based control of the collective motion of plasma electrons. Here we show that under these conditions the electron dynamics are mapped into the beam of protons accelerated via strong charge-separation-induced electrostatic fields. It is demonstrated experimentally and numerically via 3D particle-in-cell simulations that the degree of ellipticity of the laser polarization strongly influences the spatial-intensity distribution of the beam of multi-MeV protons. The influence on both sheath-accelerated and radiation pressure-accelerated protons is investigated. This approach opens up a potential new route to control laser-driven ion sources.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.