Taking wing: time to decide on the F-35 Joint Strike Fighter

Summary: The government is about to make a decision on whether to spend between $8 and 10 billion of taxpayer’s money on the new F-35 Joint Strike Fighter. It’s also an important call because it will cement the F-35 as the main instrument of Australian air-power for decades into the future. The F-35 has a troubled past—management issues and the enormous complexity of the project have caused significant cost and schedule overruns. But now it seems to be on track to come into service with the RAAF in 2020, and to be a very capable aircraft. The other option is a further purchase of less-advanced Super Hornets, which would come with a marginally lower price tag. But that choice would come at a cost to Canberra’s relationship with Washington as we pulled out of the US-run program, and provide less capability in a region replete with rapid military modernisation

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach

Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show that the model is capable of producing respiratory waveforms that approach the gold standard, while in turn producing state of the art respiratory rate estimates. We also show that when it comes to capturing more advanced respiratory waveform characteristics such as duty cycle, our model is for the most part unsuccessful. A suggested reason for this, in light of a previous study on in-ear PPG, is that the respiratory variations in finger-PPG are far weaker compared with other recording locations. Importantly, our model can perform these waveform estimates in a fraction of a millisecond, giving it the capacity to produce over 6 hours of respiratory waveforms in a single second. Moreover, we attempt to interpret the behaviour of the kernel weights within the model, showing that in part our model intuitively selects different breathing frequencies. The model proposed in this work could help to improve the usefulness of consumer PPG-based wearables for medical applications, where detailed respiratory information is required

Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning

Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices. The PPG signals provide insight into the body's circulatory system and can be employed to extract various bio-features, such as heart rate and vascular ageing. Although several algorithms have been proposed for this purpose, many exhibit limitations, including heavy reliance on human calibration, high signal quality requirements, and a lack of generalisation. In this paper, we introduce a PPG signal processing framework that integrates graph theory and computer vision algorithms, to provide an analysis framework which is amplitude-independent and invariant to affine transformations. It also requires minimal preprocessing, fuses information through RGB channels and exhibits robust generalisation across tasks and datasets. The proposed VGTL-net achieves state-of-the-art performance in the prediction of vascular ageing and demonstrates robust estimation of continuous blood pressure waveforms

‘Humour can open the door to conversations’: Exploring the Role of Comedy in Breaking Down Barriers to Employment for Young Disabled People

Young disabled people encounter many barriers in their transition to adulthood, including having access to the world of work. According to recent data, only 4.8% of adults with a learning disability are in paid work. We wanted to explore how we might address one of the initial barriers, the employment recruitment process. Eleven young disabled people were invited to work with The Comedy Trust to make their own video Curriculum Vitae (CV). We hoped to explore how we could draw on the art form of comedy to not only produce video CVs but also to share the voices of young people with a learning disability. Young people with learning disabilities have a desire to work and have the skills necessary to succeed in the workplace with appropriate support. This paper highlights the importance of employers and those who support young disabled people, having the opportunity to hear the voices of young people so they can consider how to make their recruitment process more inclusive and to address the barriers that are experienced

The Sweetest Song In The World

Illustration of music notes zig-zaging from top to bottom of the cover with a heart at the bottomhttps://scholarsjunction.msstate.edu/cht-sheet-music/10522/thumbnail.jp

A Search for Gravitational Waves from Binary Mergers with a Single Observatory

We present a search for merging compact binary gravitational-wave sources that produce a signal appearing solely or primarily in a single detector. Past analyses have heavily relied on coincidence between multiple detectors to reduce non-astrophysical background. However, for $\sim40\%$ of the total time of the 2015-2017 LIGO-Virgo observing runs only a single detector was operating. We discuss the difficulties in assigning significance and calculating the probability of astrophysical origin for candidates observed primarily by a single detector, and suggest a straightforward resolution using a noise model designed to provide a conservative assessment given the observed data. We also describe a procedure to assess candidates observed in a single detector when multiple detectors are observing. We apply these methods to search for binary black hole (BBH) and binary neutron star (BNS) mergers in the open LIGO data spanning 2015-2017. The most promising candidate from our search is 170817+03:02:46UTC (probability of astrophysical origin $p_{\rm astro} \sim 0.4$): if astrophysical, this is consistent with a BBH merger with primary mass $67_{-15}^{+21}\,M_{\odot}$, suggestive of a hierarchical merger origin. We also apply our method to the analysis of GW190425 and find $p_{\rm astro} \sim 0.5$, though this value is highly dependent on assumptions about the noise and signal models.Comment: 11 pages, 5 figures, 2 tables. Updated to match ApJ version. Supplementary materials at https://github.com/gwastro/single-searc

Estimating the incidence of colorectal cancer in Sub-Saharan Africa:A systematic analysis

Background Nearly two–thirds of annual mortality worldwide is attributable to non–communicable diseases (NCDs), with 70% estimated to occur in low– and middle–income countries (LMIC). Colorectal cancer (CRC) accounts for over 600 000 deaths annually, but data concerning cancer rates in LMIC is very poor. This study analyses the data available to produce an estimate of the incidence of colorectal cancer in Sub–Saharan Africa (SSA). Methods Data for this analysis came from two main sources: a systematic search of Medline, EMBASE and Global Health which found 15 published data sets, and an additional 42 unpublished data sets which were sourced from the IARC and individual cancer registries. Data for case rates by age and sex, as well as population denominators were extracted and analysed to produce an estimate of incidence. Results: The crude incidence of CRC in SSA for both sexes was found to be 4.04 per 100 000 population (4.38 for men and 3.69 for women). Incidence increased with age with the highest rates in Southern Africa, particularly in South Africa. The rates of CRC in SSA were much lower than those reported for high–income countries. Conclusion Few health services in SSA are equipped to provide timely diagnosis and treatment of cancer in SSA. In addition, data collection systems are weak, meaning that the available statistics may underestimate the burden of disease. In order to improve health care services it is vital that accurate measurements of disease burden are available to policy maker

Virologic failure and second-line antiretroviral therapy in children in South Africa--the IeDEA Southern Africa collaboration

Article approval pendingWith expanding pediatric antiretroviral therapy (ART) access, children will begin to experience treatment failure and require second-line therapy. We evaluated the probability and determinants of virologic failure and switching in children in South Africa