Improved 2D‐HPLC of red wine by incorporating pre‐process signal‐smoothing algorithms

Twomultidimensional HPLC separations of an Australian red wine are presented, >70% of the available separation space was used. A porous graphitic carbon (PGC) stationary phase was used as the first dimension in both separations with both RP core–shell and hydrophilic interaction chromatography fully porous columns used separately in the second dimension. To overcome peak analysis problems caused by signal noise and low detection limits, the data were pre-processed with penalised least-squares smoothing. The PGC × RP combination separated 85 peaks with a spreading angle of 71 and the PGC × hydrophilic interaction chromatography separated 207 peaks with a spreading angle of 80. Both 2D-HPLC steps were completed in 76 min using a comprehensive stop-and-go approach. A smoothing step was added to peak-picking processes and was able to greatly reduce the number of false peaks present due to noise in the chromatograms. The required thresholds were not able to ignore the noise because of the small magnitude of the peaks; 1874 peaks were located in the non-smoothed PGC × RP separation that reduced to 227 peaks after smoothing was included.

Small-signal stability assessment of active distribution networks with dynamic loads

This paper investigates small-signal stability of a distribution system with distributed generator and induction motor load, as a dynamic element. The analysis is carried out over a distribution test system with different types of induction motor loads. The system is linearised by the perturbation method. Eigenvalues and participation factors are calculated to see the modal interaction of the system. The study indicates that load voltage dynamics significantly influence the damping of a newly identified voltage mode. This mode has frequency of oscillation between the electromechanical and subsynchronous oscillation of power systems. To justify the validity of the modal analysis time domain simulation is also carried out. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.

Effect of training on activation of extracellular signal-regulated kinase 1/2 and p38 mitogen-activated protein kinase pathways in rat soleus muscle

1. The effect of a chronic programme of either low- or moderate-to-high-intensity treadmill running on the activation of the extracellular-signal regulated protein kinase (ERK1/2) and the p38 mitogen-activated protein kinase (MAPK) pathways was determined in rat muscle. 2. Sprague-Dawley rats were assigned to one of three groups: (i) sedentary (NT; n = 8); (ii) low-intensity training (8 m/min; LIT; n = 16); and (iii) moderate-to-high-intensity training (28 m/min; HIT;n = 16). The training regimens were planned so that animals covered the same distance and had similar glycogen utilization for both LIT and HIT exercise sessions. 3. A single bout of LIT or HIT following 8 weeks of training led to a twofold increase in the phosphorylation of ERK1/2 (P = 0.048) and a two- to threefold increase in p38 MAPK (P = 0.005). Extracellular signal-regulated kinase 1/2 phosphorylation in muscle sampled 48 h after the last exercise bout was similar to sedentary values, while p38 MAPK phosphorylation was 70-80% lower than sedentary. One bout of LIT or HIT increased total ERK1/2 and p38 MAPK expression, with the magnitude of this increase being independent of prior exercise intensity or duration. Extracellular signal-regulated kinase 1/2 expression was increased three- to fourfold in muscle sampled 48 h after the last exercise bout irrespective of the prior training programme (P = 0.027), but p38 MAPK expression was approximately 90% lower than sedentary values. 4. In conclusion, exercise-training of different intensities/durations results in selective postexercise activation of intracellular signalling pathways, which may be one mechanism regulating specific adaptations induced by diverse training programmes.

Granulocyte colony-stimulating factor receptor signalling via Janus kinase 2/signal transducer and activator of transcription 3 in ovarian cancer

Ovarian cancer remains a major cause of cancer mortality in women, with only limited understanding of disease aetiology at the molecular level. Granulocyte colony-stimulating factor (G-CSF) is a key regulator of both normal and emergency haematopoiesis, and is used clinically to aid haematopoietic recovery following ablative therapies for a variety of solid tumours including ovarian cancer.

ANFIS traffic signal controller for an isolated intersection

Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters during learning time. The results of the experiments show higher performance of the ANFIS traffic signal controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different values for green phase. Results show the higher performance of ANFIS controller.

Detection of respiratory arousals using photoplethysmography (PPG) signal in sleep apnea patients

Respiratory events during sleep induce cortical arousals and manifest changes in autonomic markers in sleep disorder breathing (SDB). Finger photoplethysmography (PPG) has been shown to be a reliable method of determining sympathetic activation. We hypothesize that changes in PPG signals are sufficient to predict the occurrence of respiratory-event-related cortical arousal. In this study, we develop a respiratory arousal detection model in SDB subjects by using PPG features. PPG signals from 10 SDB subjects (9 male, 1 female) with age range 43-75 years were used in this study. Time domain features of PPG signals, such as 1) PWA--pulse wave amplitude, 2) PPI--peak-to-peak interval, and 3) Area--area under peak, were used to detect arousal events. In this study, PWA and Area have shown better performance (higher accuracy and lower false rate) compared to PPI features. After investigating possible groupings of these features, combination of PWA and Area (PWA + Area) was shown to provide better accuracy with a lower false detection rate in arousal detection. PPG-based arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic with an area under the curve of 0.91. For the decision threshold (PC(thresh) = 25%) chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. The results showed an accuracy of 84.68%, 85.15%, 86.93%, and 50.79% with a false rate of 21.80%, 55.41%, 64.78%, and 50.79% at PC(thresh) = 25% or PPI, PWA, Area , and PWA + Area features, respectively. This indicates that combining PWA and Area features reduced the false positive rate without much affecting the sensitivity of the arousal detection system. In conclusion, the PPG-based respiratory arousal detection model is a simple and promising alternative to the conventional electroencephalogram (EEG)-based respiratory arousal detection system.

Blind extraction of cyclostationary signal from convolutional mixtures

Extracting a signal of interest from available measurements is a challenging problem. One property which can be utilized to extract the signal is cyclostationarity, which exists in many signals. Various blind source separation methods based on cyclostationarity have been reported in the literature but they assume that the mixing system is instantaneous. In this paper, we propose a method for blind extraction of cyclostationary signal from convolutional mixtures. Given that the signal of interest has a unique cyclostationary frequency and the sensors are placed close to the concerned signal, we show that the signal of interest can be estimated from the measured data. Simulations results show the effectiveness of our method.