Atmospheric-pressure plasma jets : effect of gas flow, active species, and snake-like bullet propagation

Cold atmospheric-pressure plasma jets have recently attracted enormous interest owing to numerous applications in plasma biology, health care, medicine, and nanotechnology. A dedicated study of the interaction between the upstream and downstream plasma plumes revealed that the active species (electrons, ions, excited OH, metastable Ar, and nitrogen-related species) generated by the upstream plasma plume enhance the propagation of the downstream plasma plume. At gas flows exceeding 2 l/min, the downstream plasma plume is longer than the upstream plasma plume. Detailed plasma diagnostics and discharge species analysis suggest that this effect is due to the electrons and ions that are generated by the upstream plasma and flow into the downstream plume. This in turn leads to the relatively higher electron density in the downstream plasma. Moreover, high-speed photography reveals a highly unusual behavior of the plasma bullets, which propagate in snake-like motions, very differently from the previous reports. This behavior is related to the hydrodynamic instability of the gas flow, which results in non-uniform distributions of long-lifetime active species in the discharge tube and of surface charges on the inner surface of the tube.

Raman-active wurtzite CdO nanophase and phonon signatures in CdO/ZnO heterostructures fabricated by nonequilibrium laser plasma ablation and stress control

A combination of laser plasma ablation and strain control in CdO/ZnO heterostructures is used to produce and stabilize a metastable wurtzite CdO nanophase. According to the Raman selection rules, this nanophase is Raman-active whereas the thermodynamically preferred rocksalt phase is inactive. The wurtzite-specific and thickness/strain-dependent Raman fingerprints and phonon modes are identified and can be used for reliable and inexpensive nanophase detection. The wurtzite nanophase formation is also confirmed by x-ray diffractometry. The demonstrated ability of the metastable phase and phonon mode control in CdO/ZnO heterostructures is promising for the development of next-generation light emitting sources and exciton-based laser diodes.

A novel matrix converter based resonant dual active bridge for V2G applications

Dual-active bridges (DABs) can be used to deliver isolated and bidirectional power to electric vehicles (EVs) or to the grid in vehicle-to-grid (V2G) applications. However, such a system essentially requires a two-stage power conversion process, which significantly increases the power losses. Furthermore, the poor power factor associated with DAB converters further reduces the efficiency of such systems. This paper proposes a novel matrix converter based resonant DAB converter that requires only a single-stage power conversion process to facilitate isolated bi-directional power transfer between EVs and the grid. The proposed converter comprises a matrix converter based front end linked with an EV side full-bridge converter through a high frequency isolation transformer and a tuned LCL network. A mathematical model, which predicts the behavior of the proposed system, is presented to show that both the magnitude and direction of the power flow can be controlled through either relative phase angle or magnitude modulation of voltages produced by converters. Viability of the proposed concept is verified through simulations. The proposed matrix converter based DAB, with a single power conversion stage, is low in cost, and suites charging and discharging in single or multiple EVs or V2G applications.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

A loop cancellation based active damping solution for constant power instability in vehicular power systems

In recent years, electric propulsion systems have increasingly been used in land, sea and air vehicles. The vehicular power systems are usually loaded with tightly regulated power electronic converters which tend to draw constant power. Since the constant power loads (CPLs) impose negative incremental resistance characteristics on the feeder system, they pose a potential threat to the stability of vehicular power systems. This effect becomes more significant in the presence of distribution lines between source and load in large vehicular power systems such as electric ships and more electric aircrafts. System transients such as sudden drop of converter side loads or increase of constant power requirement can cause complete system instability. Most of the existing research work focuses on the modeling and stabilization of DC vehicular power systems with CPLs. Only a few solutions are proposed to stabilize AC vehicular power systems with non-negligible distribution lines and CPLs. Therefore, this paper proposes a novel loop cancellation technique to eliminate constant power instability in AC vehicular power systems with a theoretically unbounded system stability region. Analysis is carried out on system stability with the proposed method and simulation results are presented to validate its effectiveness.

Virtual resistance based active damping solution for constant power instability in AC microgrids

With ever-increasing share of power electronic loads constant power instability is becoming a significant issue in microgrids, especially when they operate in the islanding mode. Transient conditions like resistive load-shedding or sudden increase of constant power loads (CPL) might destabilize the whole system. Modeling and stability analysis of AC microgrids with CPLs have already been discussed in literature. However, no effective solutions are provided to stabilize this kind of system. Therefore, this paper proposes a virtual resistance based active damping method to eliminate constant power instability in AC microgrids. Advantages and limitations of the proposed method are also discussed in detail. Simulation results are presented to validate the proposed active damping solution.

Stop and revive? The effectiveness of nap and active rest breaks for reducing driver sleepiness

Objectives The purpose for this study was to determine the relative benefit of nap and active rest breaks for reducing driver sleepiness. Methods Participants were 20 healthy young adults (20-25 years), including 8 males and 12 females. A counterbalanced within-subjects design was used such that each participant completed both conditions on separate occasions, a week apart. The effects of the countermeasures were evaluated by established physiological (EEG theta and alpha absolute power), subjective (Karolinska Sleepiness Scale), and driving performance measures (Hazard Perception Task). Participants woke at 5am, and undertook a simulated driving task for two hours; each participant then had either a 15-minute nap opportunity or a 15-minute active rest break that included 10 minutes of brisk walking, followed by another hour of simulated driving. Results The nap break reduced EEG theta and alpha absolute power and eventually reduced subjective sleepiness levels. In contrast, the active rest break did not reduce EEG theta and alpha absolute power levels with the power levels eventually increasing. An immediate reduction of subjective sleepiness was observed, with subjective sleepiness increasing during the final hour of simulated driving. No difference was found between the two breaks for hazard perception performance. Conclusions Only the nap break produced a significant reduction in physiological sleepiness. The immediate reductions of subjective sleepiness following the active rest break could leave drivers with erroneous perceptions of their sleepiness, particularly as physiological sleepiness continued to increase after the break.

Trait resilience fosters adaptive coping when control opportunities are high : implications for the motivating potential of active work

Purpose: We examine the interaction between trait resilience and control in predicting coping and performance. Drawing on a person–environment fit perspective, we hypothesized resilient individuals would cope and perform better in demanding work situations when control was high. In contrast, those low in resilience would cope and perform better when control was low. Recognizing the relationship between trait resilience and performance also could be indirect, adaptive coping was examined as a mediating mechanism through which high control enables resilient individuals to demonstrate better performance. Methodology: In Study 1 (N = 78) and Study 2 (N = 94), participants completed a demanding inbox task in which trait resilience was measured and high and low control was manipulated. Study 3 involved surveying 368 employees on their trait resilience, control, and demand at work (at Time 1), and coping and performance 1 month later at Time 2. Findings: For more resilient individuals, high control facilitated problem-focused coping (Study 1, 2, and 3), which was indirectly associated with higher subjective performance (Study 1), mastery (Study 2), adaptive, and proficient performance (Study 3). For more resilient individuals, high control also facilitated positive reappraisal (Study 2 and 3), which was indirectly associated with higher adaptive and proficient performance (Study 3). Implications: Individuals higher in resilience benefit from high control because it enables adaptive coping. Originality/value: This research makes two contributions: (1) an experimental investigation into the interaction of trait resilience and control, and (2) investigation of coping as the mechanism explaining better performance.

Fourier active appearance models

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

It wasn't my fault : does crash culpability improve predictive outcomes in a self-report study?

Introduction: Research that has focused on the ability of self-report assessment tools to predict crash outcomes has proven to be mixed. As a result, researchers are now beginning to explore whether examining culpability of crash involvement can subsequently improve this predictive efficacy. This study reports on the application of the Manchester Driver Behaviour Questionnaire (DBQ) to predict crash involvement among a sample of general Queensland motorists, and in particular, whether including a crash culpability variable improves predictive outcomes. Surveys were completed by 249 general motorists on-line or via a pen-and-paper format. Results: Consistent with previous research, a factor analysis revealed a three factor solution for the DBQ accounting for 40.5% of the overall variance. However, multivariate analysis using the DBQ revealed little predictive ability of the tool to predict crash involvement. Rather, exposure to the road was found to be predictive of crashes. An analysis into culpability revealed 88 participants reported being “at fault” for their most recent crash. Corresponding between and multi-variate analyses that included the culpability variable did not result in an improvement in identifying those involved in crashes. Conclusions: While preliminary, the results suggest that including crash culpability may not necessarily improve predictive outcomes in self-report methodologies, although it is noted the current small sample size may also have had a deleterious effect on this endeavour. This paper also outlines the need for future research (which also includes official crash and offence outcomes) to better understand the actual contribution of self-report assessment tools, and culpability variables, to understanding and improving road safety.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

An SECM study on the influence of cationic, membrane-active peptides on a gold-supported self-assembled monolayer

The influence of the membrane active peptides, Tat44–57 (activator in HIV-1) and melittin (active content of bee venom), on self-assembled monolayers of 6-mercaptohexanoic acid (MHA) on gold electrodes has been studied with scanning electrochemical microscopy (SECM). It was found that MHA, when deprotonated at physiological pH, significantly affected the relative rates of electron transfer between the [Fe(CN)6]4− solution based mediator and the underlying gold electrode, predominantly by the electrostatic interaction between the mediator and MHA. Upon the introduction of Tat44–57 ormelittin to the electrolyte, the relative rate of electron transfer through the MHA layer could be increased or decreased depending on the mediator used. However, in all cases it was found that these peptides have the ability to be incorporated into synthetic SAMs, which has implications for future electrochemical studies carried out using cell mimicking membranes immobilised on such layers.