Relativistic breather-type solitary waves with linear polarization in cold plasmas

Linearly polarized solitary waves, arising from the interaction of an intense laser pulse with a plasma, are investigated. Localized structures, in the form of exact numerical nonlinear solutions of the one-dimensional Maxwell-fluid model for a cold plasma with fixed ions, are presented. Unlike stationary circularly polarized solitary waves, the linear polarization gives rise to a breather-type behavior and a periodic exchange of electromagnetic energy and electron kinetic energy at twice the frequency of the wave. A numerical method based on a finite-differences scheme allows us to compute a branch of solutions within the frequency range Ωmin<Ω<ωpe, where ωpe and Ωmin are the electron plasma frequency and the frequency value for which the plasma density vanishes locally, respectively. A detailed description of the spatiotemporal structure of the waves and their main properties as a function of Ω is presented. Small-amplitude oscillations appearing in the tail of the solitary waves, a consequence of the linear polarization and harmonic excitation, are explained with the aid of the Akhiezer-Polovin system. Direct numerical simulations of the Maxwell-fluid model show that these solitary waves propagate without change for a long time.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Properties of extragalactic dust inferred from linear polarimetry of Type Ia Supernovae

Aims
The aim of this paper is twofold: 1) to investigate the properties of extragalactic dust and compare them to what is seen in the Galaxy; 2) to address in an independent way the problem of the anomalous extinction curves reported for reddened Type Ia Supernovae (SN) in connection to the environments in which they explode. 

Methods
The properties of the dust are derived from the wavelength dependence of the continuum polarization observed in four reddened Type Ia SN: 1986G, 2006X, 2008fp, and 2014J. The method is based on the observed fact that Type Ia SN have a negligible intrinsic continuum polarization. This and their large luminosity makes them ideal tools to probe the dust properties in extragalactic environments.

Results
All four objects are characterized by exceptionally low total-to-selective absorption ratios (R<inf>V</inf>) and display an anomalous interstellar polarization law, characterized by very blue polarization peaks. In all cases the polarization position angle is well aligned with the local spiral structure. While SN 1986G is compatible with the most extreme cases of interstellar polarization known in the Galaxy, SN 2006X, 2008fp, and 2014J show unprecedented behaviours. The observed deviations do not appear to be connected to selection effects related to the relatively large amounts of reddening characterizing the objects in the sample.

Conclusions
The dust responsible for the polarization of these four SN is most likely of interstellar nature. The polarization properties can be interpreted in terms of a significantly enhanced abundance of small grains. The anomalous behaviour is apparently associated with the properties of the galactic environment in which the SN explode, rather than with the progenitor system from which they originate. For the extreme case of SN 2014J, we cannot exclude the contribution of light scattered by local material; however, the observed polarization properties require an ad hoc geometrical dust distribution.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

The TARANIS laser : A multi-terawatt system for laser plasma physics

The Terawatt Apparatus for Relativistic And Non-linear Interdisciplinary Science (TARANIS), installed in the Centre for Plasma Physics at the Queen's University Belfast, supports a wide ranging science program, including laser-driven particle acceleration, X-ray lasers and high energy density physics experiments. We present (1) an overview of the laser facility, (2) results of preliminary investigations on proton acceleration, laser action at 13.9 nm and Kα sources and (3) speculation on future experiments using these extreme sources.

Sparse multi-carrier index keying OFDM with index separation over correlated sub-carriers

In this paper, we propose a sparse multi-carrier index keying (MCIK) method for orthogonal frequency division multiplexing (OFDM) system, which uses the indices of sparse sub-carriers to transmit the data, and improve the performance
of signal detection in highly correlated sub-carriers. Although a receiver is able to exploit a power gain with precoding in OFDM, the sensitivity of the signal detection is usually high as the orthogonality is not retained in highly dispersive
environments. To overcome this, we focus on developing the trade-off between the sparsity of the MCIK, correlation, and performances, analyzing the average probability of the error propagation imposed by incorrect index detection over highly correlated sub-carriers. In asymptotic cases, we are able to see how sparsity of MCIK should be designed in order to perform superior to the classical OFDM system. Based on this feature, sparse MCIK based OFDM is a better choice for low detection errors in highly correlated sub-carriers.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

The high-energy environment in the super-Earth system CoRoT-7

High-energy irradiation of exoplanets has been identified to be a key influence on the stability of these planets' atmospheres. So far, irradiation-driven mass-loss has been observed only in two Hot Jupiters, and the observational data remain even more sparse in the super-Earth regime. We present an investigation of the high-energy emission in the CoRoT-7 system, which hosts the first known transiting super-Earth. To characterize the high-energy XUV radiation field into which the rocky planets CoRoT-7b and CoRoT-7c are immersed, we analyzed a 25 ks XMM-Newton observation of the host star. Our analysis yields the first clear (3.5σ) X-ray detection of CoRoT-7. We determine a coronal temperature of ≈ 3 MK and an X-ray luminosity of 3 × 1028 erg s-1. The level of XUV irradiation on CoRoT-7b amounts to ≈37 000 erg cm-2 s-1. Current theories for planetary evaporation can only provide an order-of-magnitude estimate for the planetary mass loss; assuming that CoRoT-7b has formed as a rocky planet, we estimate that CoRoT-7b evaporates at a rate of about 1.3 × 1011 g s-1 and has lost ≈4-10 earth masses in total.

Novel system for distant assessment of cataract surgical quality in rural China

BACKGROUND: This study aims to assess the quality of various steps of manual small incision cataract surgery and predictors of quality, using video recordings.
DESIGN: This paper applies a retrospective study.
PARTICIPANTS: Fifty-two trainees participated in a hands-on small incision cataract surgery training programme at rural Chinese hospitals.
METHODS: Trainees provided one video each recorded by a tripod-mounted digital recorder after completing a one-week theoretical course and hands-on training monitored by expert trainers. Videos were graded by two different experts, using a 4-point scale developed by the International Council of Ophthalmology for each of 12 surgical steps and six global factors. Grades ranged from 2 (worst) to 5 (best), with a score of 0 if the step was performed by trainers.
MAIN OUTCOME MEASURES: Mean score for the performance of each cataract surgical step rated by trainers.
RESULTS: Videos and data were available for 49/52 trainees (94.2%, median age 38 years, 16.3% women and 77.5% completing > 50 training cases). The majority (53.1%, 26/49) had performed ≤ 50 cataract surgeries prior to training. Kappa was 0.57∼0.98 for the steps (mean 0.85). Poorest-rated steps were draping the surgical field (mean ± standard deviation = 3.27 ± 0.78), hydro-dissection (3.88 ± 1.22) and wound closure (3.92 ± 1.03), and top-rated steps were insertion of viscoelastic (4.96 ± 0.20) and anterior chamber entry (4.69 ± 0.74). In linear regression models, higher total score was associated with younger age (P = 0.015) and having performed >50 independent manual small incision cases (P = 0.039).
CONCLUSIONS: More training should be given to preoperative draping, which is poorly performed and crucial in preventing infection. Surgical experience improves ratings.© 2015 Royal Australian and New Zealand College of Ophthalmologists.