Bubbly Flow Structure in Hydraulic Jump

In an open channel, a hydraulic jump is the rapid transition from super- to sub-critical flow associated with strong turbulence and air bubble entrainment in the mixing layer. New experiments were performed at relatively large Reynolds numbers using phase-detection probes. Some new signal analysis provided characteristic air-water time and length scales of the vortical structures advecting the air bubbles in the developing shear flow. An analysis of the longitudinal air-water flow structure suggested little bubble clustering in the mixing layer, although an interparticle arrival time analysis showed some preferential bubble clustering for small bubbles with chord times below 3 ms. Correlation analyses yielded longitudinal air-water time scales Txx*V1/d1 of about 0.8 in average. The transverse integral length scale Z/d1 of the eddies advecting entrained bubbles was typically between 0.25 and 0.4, irrespective of the inflow conditions within the range of the investigations. Overall the findings highlighted the complicated nature of the air-water flow

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

Turbulence Characterisation of High-Velocity Free-Surface Flows

In high-velocity free-surface flows, air is continuously being trapped and released through the free-surface. Such high-velocity highly-aerated flows cannot be studied numerically because of the large number of relevant equations and parameters. Herein an advanced signal processing of traditional single- and dual-tip conductivity probes provides some new information on the air-water turbulent time and length scales. The technique is applied to turbulent open channel flows in a large-size facility. The auto- and cross-correlation analyses yield some characterisation of the large eddies advecting the bubbles. The transverse integral turbulent length and time scales are related to the step height: i.e., Lxy/h ~ 0.02 to 0.2, and T.sqrt(g/h) ~ 0.004 to 0.04. The results are irrespective of the Reynolds numbers. The present findings emphasise that turbulent dissipation by large-scale vortices is a significant process in the intermediate zone between the spray and bubbly flow regions (0.3 < C < 0.7). Some self-similar relationships were observed systematically at both macroscopic and microscopic levels. The results are significant because they provide a picture general enough to be used to characterise the air-water flow field in prototype spillways.

Psychological science in Australia

This paper is a brief account of the Australian Strategic Review of the Psychology Discipline and its Research. Issues discussed include Australia's contribution to world research in psychological science, research funding, the age and qualifications of researchers, the social and economic benefits arising from the applications of research outcomes, multidisciplinary issues, the role of psychology in industry, links between the discipline and the profession of psychology, professional training, cross-cultural issues, and educational issues. Priorities for development of research and postgraduate training are identified, and include human factors and other areas related to technological change, clinical psychology with particular attention to health psychology, industrial and organizational psychology, and neuroscience and physiological psychology.

Short-term spatial diffusion in sigma o+ o- optical molasses

We have measured the spatial diffusion of atoms in a three-dimensional sigma(+)-sigma(-) optical molasses over twenty milliseconds timescale, starting from the initial interaction of the atoms with the molasses. We find that the diffusion constants agree well with a linear model for these short time scales and also compare favourably to other studies of diffusion made over longer time scales. These measurements enable us to quantify the detection method known as freezing molasses. We discuss this method, for detecting and measuring the momentum distribution of cold atoms, which relies on the slow diffusion of atoms in optical molasses to produce a freeze-frame of the spatial distribution of the atoms. This method enables a longer interrogation interval, providing a greatly increased signal-to-noise ratio. (C) 1998 Elsevier Science B.V.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Specific PCR based detection of Phytophthora medicaginis using the intergenic spacer region of the ribosomal DNA

A technique based on the polymerase chain reaction (PCR) for the specific detection of Phytophthora medicaginis was developed using nucleotide sequence information of the ribosomal DNA (rDNA) regions. The complete IGS 2 region between the 5 S gene of one rDNA repeat and the small subunit of the adjacent repeat was sequenced for P. medicaginis and related species. The entire nucleotide sequence length of the IGS 2 of P. medicaginis was 3566 bp. A pair of oligonucleotide primers (PPED04 and PPED05), which allowed amplification of a specific fragment (364 bp) within the IGS 2 of P. medicaginis using the PCR, was designed. Specific amplification of this fragment from P. medicaginis was highly sensitive, detecting template DNA as low as 4 ng and in a host-pathogen DNA ratio of 1000000:1. Specific PCR amplification using PPED04 and PPED05 was successful in detecting P. medicaginis in lucerne stems infected under glasshouse conditions and field infected lucerne roots. The procedures developed in this work have application to improved identification and detection of a wide range of Phytophthora spp. in plants and soil.

Psychological distress among female sex workers

The prevalence and correlates of psychological distress were examined in a sample of 171 female sex workers in Queensland. It was found that 28 per cent were above the GHQ-28 threshold for mild psychiatric morbidity, a rate that is not appreciably different from that of women in the general community. The sample included only eight street sex workers, all of whom reported significant distress. Logistic regression analyses showed that a history of injecting drug use, an early age at leaving home and wanting to leave the sex industry were independent predictors of poor mental health. Distressed sex workers reported fewer sexual health examinations and less consistent condom use with their clients than those who were not distressed.