Nitrogen-doped graphene films from chemical vapor deposition of pyridine: Influence of process parameters on the electrical and optical properties

Graphene films were produced by chemical vapor deposition (CVD) of pyridine on copper substrates. Pyridine-CVD is expected to lead to doped graphene by the insertion of nitrogen atoms in the growing sp2 carbon lattice, possibly improving the properties of graphene as a transparent conductive film. We here report on the influence that the CVD parameters (i.e., temperature and gas flow) have on the morphology, transmittance, and electrical conductivity of the graphene films grown with pyridine. A temperature range between 930 and 1070 °C was explored and the results were compared to those of pristine graphene grown by ethanol-CVD under the same process conditions. The films were characterized by atomic force microscopy, Raman and X-ray photoemission spectroscopy. The optical transmittance and electrical conductivity of the films were measured to evaluate their performance as transparent conductive electrodes. Graphene films grown by pyridine reached an electrical conductivity of 14.3 × 105 S/m. Such a high conductivity seems to be associated with the electronic doping induced by substitutional nitrogen atoms. In particular, at 930 °C the nitrogen/carbon ratio of pyridine-grown graphene reaches 3%, and its electrical conductivity is 40% higher than that of pristine graphene grown from ethanol-CVD.

Effects of electrical muscle stimulation on oxygen consumption

Electrical muscle stimulation (EMS) devices are being marketed as weight/ fat loss devices throughout the world. Commercially available stimulators have the ability to evoke muscle contractions that may affect caloric expenditure while the device is being used. The aim of this study was to test the effects of two different EMS devices (Abtronic and Feminique) on oxygen consumption at rest. Subjects arrived for testing after an overnight fast, had the devices fitted, and then positioned supine with expired air measured to determine oxygen consumption. After a 10-minute acclimation period, oxygen consumption was measured for 20 minutes with the device switched off (resting) then 20 minutes with the device switched on (stimulated). There were no significant differences (p > 0.05) in oxygen consumption between the resting and stimulated periods with either the Abtronic (mean +/- SD; resting, 3.40 +/- 0.44; stimulated, 3.45 +/- 0.53 ml of O2[middle dot]kg-1[middle dot]min-1) or the Feminique (resting, 3.73 +/- 0.45; stimulated, 3.75 +/- 0.46 ml of O2[middle dot]kg-1[middle dot]min-1). In summary, the EMS devices tested had no effect on oxygen consumption during muscle stimulation.

Effects of increasing neuromuscular electrical stimulation current intensity on cortical sensorimotor network activation: A time domain fNIRS study

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O₂Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O₂Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O₂Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease

Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.

Identifying safety beliefs among Australian electrical workers

The current study explored underlying beliefs regarding work safety among a sample of experienced Australian electrical workers. A qualitative research methodology using the theory of planned behavior as a framework was employed. A series of interviews and focus groups with licensed electrical workers (N = 46) were analyzed using thematic content analysis. Beliefs were classified as advantages (e.g. personal safety of self and co-workers), disadvantages (e.g., inconvenience to customer/clients and workload), referents (e.g., supervisors, work colleagues, customers), barriers (e.g., time and cost), and facilitators (e.g., training and knowledge, equipment availability) of safety adherence. The belief basis of the theory of planned behavior was a useful framework for exploring workers’ safety beliefs. The identified beliefs can inform future research about the important factors influencing safe work decisions and inform strategies to promote safer workplace decision making within the electrical safety context.

Single layer bismuth iodide: Computational exploration of structural, electrical, mechanical and optical properties

Layered graphitic materials exhibit new intriguing electronic structure and the search for new types of two-dimensional (2D) monolayer is of importance for the fabrication of next generation miniature electronic and optoelectronic devices. By means of density functional theory (DFT) computations, we investigated in detail the structural, electronic, mechanical and optical properties of the single-layer bismuth iodide (BiI3) nanosheet. Monolayer BiI3 is dynamically stable as confirmed by the computed phonon spectrum. The cleavage energy (Ecl) and interlayer coupling strength of bulk BiI3 are comparable to the experimental values of graphite, which indicates that the exfoliation of BiI3 is highly feasible. The obtained stress-strain curve shows that the BiI3 nanosheet is a brittle material with a breaking strain of 13%. The BiI3 monolayer has an indirect band gap of 1.57 eV with spin orbit coupling (SOC), indicating its potential application for solar cells. Furthermore, the band gap of BiI3 monolayer can be modulated by biaxial strain. Most interestingly, interfacing electrically active graphene with monolayer BiI3 nanosheet leads to enhanced light absorption compared to that in pure monolayer BiI3 nanosheet, highlighting its great potential applications in photonics and photovoltaic solar cells.

Electrical conduction in plasma polymerized thin films of γ-terpinene

Plasma polymerized c-terpinene (pp2GT) thin films are fabricated using RF plasma polymerization. MIM structures are fabricated and using the capacitive structures dielectric properties of the material is studied. The dielectric constant values are found to be in good agreement with those determined from ellipsometric data. At a frequency of 100 kHz, the dielectric constant varies with RF deposition power, from 3.69 (10 W) to 3.24 (75 W). The current density–voltage (J2V) characteristics of pp–GT thin films are investigated as a function of RF deposition power at room temperature to determine the resistivity and DC conduction mechanism of the films. At higher applied voltage region, Schottky conduction is the dominant DC conduction mechanism. The capacitance and the loss tangent are found to be frequency dependent. The conductivity of the pp2GT thin films is found to decrease from 1.39 3 10212 S/cm (10 W) to 1.02 3 10213 S/cm (75 W) and attributed to the change in the chemical composition and structure of the polymer. The breakdown field for pp–GT thin films increases from 1.48 MV/cm (10 W) to 2 MV/cm (75 W). A single broad relaxation peak is observed indicating the contribution of multiple relaxations to the dielectric response for temperature dependent J2V. The distribution of these relaxation times is determined through regularization methods. VC 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42318.

Low-frequency electrical stimulation combined with a cooling vest improves recovery of elite kayakers following a simulated 1000-m race in a hot environment

This study compared the effects of a low-frequency electrical stimulation (LFES; Veinoplus® Sport, Ad Rem Technology, Paris, France), a low-frequency electrical stimulation combined with a cooling vest (LFESCR) and an active recovery combined with a cooling vest (ACTCR) as recovery strategies on performance (racing time and pacing strategies), physiologic and perceptual responses between two sprint kayak simulated races, in a hot environment (∼32 wet-bulb-globe temperature). Eight elite male kayakers performed two successive 1000-m kayak time trials (TT1 and TT2), separated by a short-term recovery period, including a 30-min of the respective recovery intervention protocol, in a randomized crossover design. Racing time, power output, and stroke rate were recorded for each time trial. Blood lactate concentration, pH, core, skin and body temperatures were measured before and after both TT1 and TT2 and at mid- and post-recovery intervention. Perceptual ratings of thermal sensation were also collected. LFESCR was associated with a very likely effect in performance restoration compared with ACTCR (99/0/1%) and LFES conditions (98/0/2%). LFESCR induced a significant decrease in body temperature and thermal sensation at post-recovery intervention, which is not observed in ACTCR condition. In conclusion, the combination of LFES and wearing a cooling vest (LFESCR) improves performance restoration between two 1000-m kayak time trials achieved by elite athletes, in the heat.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Formal network behaviour analysis using model checking

In this research we modelled computer network devices to ensure their communication behaviours meet various network standards. By modelling devices as finite-state machines and examining their properties in a range of configurations, we discovered a flaw in a common network protocol and produced a technique to improve organisations' network security against data theft.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.