Statistical modeling to determine resonant frequency information present in combustion chamber pressure signals

A statistical modeling method to accurately determine combustion chamber resonance is proposed and demonstrated. This method utilises Markov-chain Monte Carlo (MCMC) through the use of the Metropolis-Hastings (MH) algorithm to yield a probability density function for the combustion chamber frequency and find the best estimate of the resonant frequency, along with uncertainty. The accurate determination of combustion chamber resonance is then used to investigate various engine phenomena, with appropriate uncertainty, for a range of engine cycles. It is shown that, when operating on various ethanol/diesel fuel combinations, a 20% substitution yields the least amount of inter-cycle variability, in relation to combustion chamber resonance.

Magnocellular and parvocellular pathway mediated luminance contrast discrimination in amblyopia

To evaluate whether luminance contrast discrimination losses in amblyopia on putative magnocellular (MC) and parvocellular (PC) pathway tasks reflect deficits at retinogeniculate or cortical sites. Fifteen amblyopes including six anisometropes, seven strabismics, two mixed and 12 age-matched controls were investigated. Contrast discrimination was measured using established psychophysical procedures that differentiate MC and PC processing. Data were described with a model of the contrast response of primate retinal ganglion cells. All amblyopes and controls displayed the same contrast signatures on the MC and PC tasks, with three strabismics having reduced sensitivity. Amblyopic PC contrast gain was similar to electrophysiological estimates from visually normal, non-human primates. Sensitivity losses evident in a subset of the amblyopes reflect cortical summation deficits, with no change in retinogeniculate contrast responses. The data do not support the proposal that amblyopic contrast sensitivity losses on MC and PC tasks reflect retinogeniculate deficits, but rather are due to anomalous post-retinogeniculate cortical processing of retinal signals.

The role of cardiopulmonary signals in the dynamics of the eye's wavefront aberrations

Abstract—The role of cardiopulmonary signals in the dynamics of wavefront aberrations in the eye has been examined. Synchronous measurement of the eye’s wavefront aberrations, cardiac function, blood pulse, and respiration signals were taken for a group of young, healthy subjects. Two focusing stimuli, three breathing patterns, as well as natural and cycloplegic eye conditions were examined. A set of tools, including time–frequency coherence and its metrics, has been proposed to acquire a detailed picture of the interactions of the cardiopulmonary system with the eye’s wavefront aberrations. The results showed that the coherence of the blood pulse and its harmonics with the eye’s aberrations was, on average, weak (0.4 ± 0.15), while the coherence of the respiration signal with eye’s aberrations was, on average, moderate (0.53 ± 0.14). It was also revealed that there were significant intervals during which high coherence occurred. On average, the coherence was high (>0.75) during 16% of the recorded time, for the blood pulse, and 34% of the time for the respiration signal. A statistically significant decrease in average coherence was noted for the eye’s aberrations with respiration in the case of fast controlled breathing (0.5 Hz). The coherence between the blood pulse and the defocus was significantly larger for the far target than for the near target condition. After cycloplegia, the coherence of defocus with the blood pulse significantly decreased, while this was not the case for the other aberrations. There was also a noticeable, but not statistically significant, increase in the coherence of the comatic term and respiration in that case. By using nonstationary measures of signal coherence, a more detailed picture of interactions between the cardiopulmonary signals and eye’s wavefront aberrations has emerged.

Theoretical analysis of the motion and evaporation of exhaled respiratory droplets of mixed composition

The dynamics of droplets exhaled from the respiratory system during coughing or talking is addressed. A mathematical model is presented accounting for the motion of a droplet in conjunction with its evaporation. Droplet evaporation and motion are accounted for under two scenarios: 1) A well mixed droplet and 2) A droplet with inner composition variation. A multiple shells model was implemented to account for internal mass and heat transfer and for concentration and temperature gradients inside the droplet. The trajectories of the droplets are computed for a range of conditions and the spatial distribution and residence times of such droplets are evaluated.

The impact of socialisation on graduates’ public service motivation : a mixed method study

There is a renaissance of interest in public service motivation in public management research. Moynnihan and Pandey (2007) assert that public service motivation (PSM) has significant practical relevance as it deals with the relationship between motivation and the public interest. There is a need to explore employee needs generated by public service motivation in order to attract and retain a high calibre cadre of public servants (Gabris & Simo, 1995). Such exploration is particularly important beyond the American context which has dominated the literature to date (Taylor, 2007; Vandenabeele, Scheepers, & Hondeghem, 2006; Vandenabeele & Van de Walle, 2008).

Enhancing acoustic emission signals from multi-cylinder diesel engine

This paper presents techniques which can be viewed as pre-processing step towards diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time-frequency analysis, selection of optimum frequency band. Some results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals are also outlined. The results on separation of RMS signals show this technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Tracking employees’ twists and turns : a mixed methods approach to assessing change in the psychological contract

The psychological contract has emerged over the past 60 years as a key analytical device for both academics and practitioners to conceptualise and explain the employment relationship. However, despite the recognised import of this field, some authors suggest it has fallen into a ‘methodological rut’ and is neglecting to empirically assess basic theoretical tenets of the concept – such as the temporal and individualised, subjective nature of the construct. This paper describes the research design of a longitudinal, mixed methods study to explore development and change in the psychological contract and outline how the use of individual growth modelling can be a powerful tool in analysing the type of quantitative data collected. Finally, by briefly outlining the benefits of this approach, the paper seeks to offer an alternative methodology to explore the dynamic and intra-individual processes within the psychological contract domain.

Information transfer for multi-trauma patients on discharge from the emergency department : mixed-method narrative review

Aim. This paper is a report of a review conducted to identify (a) best practice in information transfer from the emergency department for multi-trauma patients; (b) conduits and barriers to information transfer in trauma care and related settings; and (c) interventions that have an impact on information communication at handover and beyond. Background. Information transfer is integral to effective trauma care, and communication breakdown results in important challenges to this. However, evidence of adequacy of structures and processes to ensure transfer of patient information through the acute phase of trauma care is limited. Data sources. Papers were sourced from a search of 12 online databases and scanning references from relevant papers for 1990–2009. Review methods. The review was conducted according to the University of York’s Centre for Reviews and Dissemination guidelines. Studies were included if they concerned issues that influenced information transfer for patients in healthcare settings. Results. Forty-five research papers, four literature reviews and one policy statement were found to be relevant to parts of the topic, but not all of it. The main issues emerging concerned the impact of communication breakdown in some form, and included communication issues within trauma team processes, lack of structure and clarity during handovers including missing, irrelevant and inaccurate information, distractions and poorly documented care. Conclusion. Many factors influence information transfer but are poorly identified in relation to trauma care. The measurement of information transfer, which is integral to patient handover, has not been the focus of research to date. Nonetheless, documented patient information is considered evidence of care and a resource that affects continuing care.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Innovation networks in the construction arena : the strategic management of mixed enterprises

The building and construction sector is one of the five largest contributors to the Australian economy and is a key performance component in the economy of many other jurisdictions. However, the ongoing viability of this sector is increasingly reliant on its ability to foster and transfer innovated products and practices. Interorganisational networks, which bring together key industry stakeholders and facilitate the flows of information, resources and trust necessary to secure innovation, have emerged as a key growth strategy within this and other arenas. The blending of organisations, resources and purposes creates new, hybrid institutional forms that draw on a mix of contract, structure and interpersonal relationship as integration processes. This paper argues that hybrid networked arrangements, because they incorporate relational elements, require management strategies and techniques that not always synonymous with conventional management approaches, including those used within the building and construction sector. It traces the emergence of the Construction Innovation Project in Australia as a hybrid institutional arrangement moulding public, private and academic stakeholders of the building and construction industry into a coherent collective force aimed at fostering innovation and its application within all levels of the industry. Specifically, the paper examines the Construction Innovation Project to ascertain the impact of relational governance and its management to harness and leverage the skills, resources and capacities of members to secure innovative outcomes. Finally, the paper offers some prospects to guide the ongoing work of this body and any other charged with a similar integrative responsibility.