First-Order Reversal Curve Probing of Spatially Resolved Polarization Switching Dynamics in Ferroelectric Nanocapacitors

Spatially resolved polarization switching In ferroelectric nanocapacitors was studied on the sub-25 nm scale using the first-order reversal curve (FORC) method. The chosen capacitor geometry allows both high-veracity observation of the domain structure and mapping of polarization switching in a uniform field, synergistically combining microstructural observations and probing of uniform-field polarization responses as relevant to device operation. A classical Kolmogorov-Avrami-Ishibashi model has been adapted to the voltage domain, and the individual switching dynamics of the FORC response curves are well approximated by the adapted model. The comparison with microstructures suggests a strong spatial variability of the switching dynamics inside the nanocapacitors.

Novel Generic Bounds on the Sum Rate of MIMO ZF Receivers

This paper introduces some novel upper and lower bounds on the achievable sum rate of multiple-input multiple-output (MIMO) systems with zero-forcing (ZF) receivers. The presented bounds are not only tractable but also generic since they apply for different fading models of interest, such as uncorrelated/ correlated Rayleigh fading and Ricean fading. We further formulate a new relationship between the sum rate and the first negative moment of the unordered eigenvalue of the instantaneous correlation matrix. The derived expressions are explicitly compared with some existing results on MIMO systems operating with optimal and minimum mean-squared error (MMSE) receivers. Based on our analytical results, we gain valuable insights into the implications of the model parameters, such as the number of antennas, spatial correlation and Ricean-K factor, on the sum rate of MIMO ZF receivers. © 2011 IEEE.

Capability Curve Based Enhanced Reactive Power Control Strategy for Stability Enhancement and Network Voltage Management

Reactive power has become a vital resource in modern electricity networks due to increased penetration of distributed generation. This paper examines the extended reactive power capability of DFIGs to improve network stability and capability to manage network voltage profile during transient faults and dynamic operating conditions. A coordinated reactive power controller is designed by considering the reactive power capabilities of the rotor-side converter (RSC) and the grid-side converter (GSC) of the DFIG in order to maximise the reactive power support from DFIGs. The study has illustrated that, a significant reactive power contribution can be obtained from partially loaded DFIG wind farms for stability enhancement by using the proposed capability curve based reactive power controller; hence DFIG wind farms can function as vital dynamic reactive power resources for power utilities without commissioning additional dynamic reactive power devices. Several network adaptive droop control schemes are also proposed for network voltage management and their performance has been investigated during variable wind conditions. Furthermore, the influence of reactive power capability on network adaptive droop control strategies has been investigated and it has also been shown that enhanced reactive power capability of DFIGs can substantially improve the voltage control performance.

A New Principal Curve Algorithm for Nonlinear Principal Component Analysis

This paper emerged from work supported by EPSRC grant GR/S84354/01 and proposes a method of determining principal curves, using spline functions, in principal component analysis (PCA) for the representation of non-linear behaviour in process monitoring. Although principal curves are well established, they are difficult to implement in practice if a large number of variables are analysed. The significant contribution of this paper is that the proposed method has minimal complexity, assuming simple spline geometry, thus enabling efficient computation. The paper provides a foundation for further work where multiple curves may be required to represent underlying non-linear information in complex data.

Comparison of progenitor mass estimates for the type IIP SN 2012A

We present the one-year long observing campaign of SN 2012A which exploded in the nearby (9.8 Mpc) irregular galaxy NGC 3239. The photometric evolution is that of a normal type IIP supernova. The absolute maximum magnitude, with MB = -16.23 +- 0.16 mag. SN2012A reached a peak luminosity of about 2X10**42 erg/s, which is brighter than those of other SNe with a similar 56Ni mass. The latter was estimated from the luminosity in the exponential tail of the light curve and found to be M(56Ni) = 0.011 +-0.004 Msun. The spectral evolution of SN 2012A is also typical of SN IIP, from the early spectra dominated by a blue continuum and very broad (~10**4 km/s) Balmer lines, to the late-photospheric spectra characterized by prominent P-Cygni features of metal lines (Fe II, Sc II, Ba II, Ti II, Ca II, Na ID). The photospheric velocity is moderately low, ~3X10**3 km/s at 50 days, for the low optical depth metal lines. The nebular spectrum obtained 394 days after the shock breakout shows the typical features of SNe IIP and the strength of the [O I] doublet suggests a progenitor of intermediate mass, similar to SN 2004et (~15 Msun). A candidate progenitor for SN 2012A has been identified in deep, pre-explosion K'-band Gemini North (NIRI) images, and found to be consistent with a star with a bolometric magnitude -7.08+-0.36 (log L/Lsun = 4.73 +- 0.14$ dex). The magnitude of the recovered progenitor in archival images points toward a moderate-mass 10.5 (-2/+4.5) Msun star as the precursor of SN 2012A. The explosion parameters and progenitor mass were also estimated by means of a hydrodynamical model, fitting the bolometric light curve, the velocity and the temperature evolution. We found a best fit for a kinetic energy of 0.48 foe, an initial radius of 1.8X10**13 cm and ejecta mass of 12.5 Msun.

Asynchrony between the Greenland ice core chronology and the radiocarbon calibration curve? Evidence from Irish bogs

A significant cold event, deduced from the Greenland ice cores, took place between 8200 and 8000 cal. BP. Modeling of the event suggests that higher northern latitudes would have also experienced considerable decreases in precipitation and that Ireland would have witnessed one of the greatest depressions. However, no well-dated proxy record exists from the British Isles to test the model results. Here we present independent evidence for a phase of major pine recruitment on Irish bogs at around 8150 cal. BP. Dendrochronological dating of subfossil trees from three sites reveal synchronicity in germination across the region, indicative of a regional forcing, and allows for high-precision radiocarbon based dating. The inner-rings of 40% of all samples from the north of Ireland dating to the period 8500-7500 cal. BP fall within a 25-yr window. The concurrent colonization of pine on peatland is interpreted as drier conditions in the region and provides the first substantive proxy data in support of a significant hydrological change in the north of Ireland accompanying the 8.2 ka event. The dating uncertainties associated with the Irish pine record and the Greenland Ice Core Chronology 2005 (GICC05) do not allow for any overlap between the two. Our results indicate that the discrepancy could be an artifact of dating inaccuracy, and support a similar claim by Lohne et al. (2013) for the Younger Dryas boundaries. If real, this asynchrony will most likely have affected interpretations of previous proxy alignments.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.

A Bayesian method for detecting stellar flares

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of 'quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

Rapid single-nucleotide polymorphism-based identification of clonal Pseudomonas aeruginosa isolates from patients with cystic fibrosis by the use of real-time PCR and high-resolution melting curve analysis

Pseudomonas aeruginosa genotyping relies mainly upon DNA fingerprinting methods, which can be subjective, expensive and time-consuming. The detection of at least three different clonal P. aeruginosa strains in patients attending two cystic fibrosis (CF) centres in a single Australian city prompted the design of a non-gel-based PCR method to enable clinical microbiology laboratories to readily identify these clonal strains. We designed a detection method utilizing heat-denatured P. aeruginosa isolates and a ten-single-nucleotide polymorphism (SNP) profile. Strain differences were detected by SYBR Green-based real-time PCR and high-resolution melting curve analysis (HRM10SNP assay). Overall, 106 P. aeruginosa sputum isolates collected from 74 patients with CF, as well as five reference strains, were analysed with the HRM10SNP assay, and the results were compared with those obtained by pulsed-field gel electrophoresis (PFGE). The HRM10SNP assay accurately identified all 45 isolates as members of one of the three major clonal strains characterized by PFGE in two Brisbane CF centres (Australian epidemic strain-1, Australian epidemic strain-2 and P42) from 61 other P. aeruginosa strains from Australian CF patients and two representative overseas epidemic strain isolates. The HRM10SNP method is simple, is relatively inexpensive and can be completed in <3 h. In our setting, it could be made easily available for clinical microbiology laboratories to screen for local P. aeruginosa strains and to guide infection control policies. Further studies are needed to determine whether the HRM10SNP assay can also be modified to detect additional clonal strains that are prevalent in other CF centres.

The whole song is greater than the sum of its parts: Local and structural features in music listening

Four experiments reported here demonstrate the importance of structural as well as local features in listening to contemporary popular music. Experiment 1 established that listeners without formal musical training regard as salient the formal structure that links individual sections of songs. When asked to listen to and assemble the individual sections of unfamiliar contemporary songs to form new compositions, participants positioned the sections in ways consistent with the true structure of the music. In Experiment 2, participants were provided with only the song lyrics with which to arrange the individual sections of contemporary songs. It was found that in addition to musical features
studied in Experiment 1, lyrical content of contemporary music also acts as a strong cue to a song’s formal structure. Experiments 3 and 4 revealed that listeners’ enjoyment of music is influenced both by structural features and local features of music, which were carried by the individual song sections.
The influence of structural features on music listening was most apparent over repeated hearings. In Experiment 4, listeners’ liking for contemporary music followed an inverted U-shape trend with repeated exposure, in which liking for music took a downward turn after just four repeated hearings. In contrast, liking for restructured music increased with repeated hearings and almost eliminated an initial negative effect of restructuring by the sixth hearing. In sum, our findings demonstrate that structural features as well as local features of contemporary music are salient and important to
listeners.

Sector analysis provides additional spatial information on the permanent prostate brachytherapy learning curve

PURPOSE: To evaluate the permanent prostate brachytherapy (PPB) learning curve using postimplant multisector dosimetric analysis and to assess the correlation between sector -specific dosimetry and patient-reported outcome measures (PROMs).

METHODS AND METHODS: First 200 patients treated with (125)I PPB monotherapy (145 Gy) at a single institution were assessed. Postimplant dosimetry (PID) using CT was evaluated for whole prostate (global) and 12 sectors, assessing minimum dose to 90% of prostate (D90) and dose to 0.1 cm(3) of rectum (D0.1cc). Global and sector PID results were evaluated to investigate changes in D90 with case number. Urinary and bowel PROMs were assessed using the International Prostate Symptom Score and the Expanded Prostate Cancer Index Composite questionnaire. The correlation between global and individual sector PID and urinary/bowel PROMs was also evaluated.

RESULTS: Linear regression confirmed a significant improvement in global D90 with case number (r(2) = 0.20; p = 0.001) at a rate of 0.11 Gy/case. Postimplant D90 of base sectors increased at a rate of 0.11-0.15 Gy/case (p = 0.0001) and matched global improvement. The regression lines of midgland and apex sectors were significantly different from global D90 (p = 0.01). Posterior midgland sectors showed a significant reduction in D90 with case number at a rate of 0.13-0.19 Gy/case (p = 0.01). Dose to posterior midgland sectors correlated with rectal D0.1cc dose but not bowel PROMs. Dose to posterior midgland sectors correlated with urinary International Prostate Symptom Score change, which was not apparent when global D90 alone was considered.

CONCLUSIONS: Sector analysis provided increased spatial information regarding the PPB learning curve. Furthermore, sector analysis correlated with urinary PROMs and rectal dose.