An experimental and analytical study of visual detection in a spacecraft environment, 1 July 1968 - 1 July 1969

Predicting star magnitude which can be seen with naked eye or sextant through spacecraft windo

Elastodynamics of radially inhomogeneous spherically anisotropic elastic materials in the Stroh formalism

A method is presented for solving elastodynamic problems in radially inhomogeneous elastic materials with spherical anisotropy, i.e.\ materials such that $c_{ijkl}= c_{ijkl}(r)$ in a spherical coordinate system ${r,\theta,\phi}$. The time harmonic displacement field $\mathbf{u}(r,\theta ,\phi)$ is expanded in a separation of variables form with dependence on $\theta,\phi$ described by vector spherical harmonics with $r$-dependent amplitudes. It is proved that such separation of variables solution is generally possible only if the spherical anisotropy is restricted to transverse isotropy with the principal axis in the radial direction, in which case the amplitudes are determined by a first-order ordinary differential system. Restricted forms of the displacement field, such as $\mathbf{u}(r,\theta)$, admit this type of separation of variables solutions for certain lower material symmetries. These results extend the Stroh formalism of elastodynamics in rectangular and cylindrical systems to spherical coordinates.Comment: 15 page

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

Principal Component Analysis with Noisy and/or Missing Data

We present a method for performing Principal Component Analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that compared to classic PCA, the resulting eigenvectors are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data is simply the limiting case of weight=0. The underlying algorithm is a noise weighted Expectation Maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution. We present applications of this method on simulated data and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to bibliograph

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

Integrative Whole Person Oncology Care in the UK

The term ‘whole person cancer care’ - an approach that addresses the needs of the person as well as treating the disease - is more widely understood in the UK than its synonym ‘integrative oncology”. The National Health Service (NHS), provides free access to care for all, which makes it harder to prioritise NHS funding of whole person medicine, where interventions may be multi-modal and lacking in cost-effectiveness data. Despite this, around 30% of cancer patients are known to use some form of complementary or alternative medicine (CAM). This is virtually never medically led, and usually without the support or even the knowledge of their oncology teams, with the exception of one or two large cancer centres. UK oncology services are, however, starting to be influenced from three sides; firstly, by well-developed and more holistic palliative care services; secondly, by directives from central government via the sustainable healthcare agenda; and thirdly, by increasing pressure from patient-led groups and cancer charities. CAM remains unlikely to be provided through the NHS, but nutrition, physical activity, mindfulness, and stress management are already becoming a core part of the NHS ‘Living With and Beyond Cancer’ agenda. This supports cancer survivors into stratified pathways of care, based on individual, self-reported holistic needs and risk assessments, which are shared between healthcare professionals and patients. Health and Wellbeing events are being built into cancer care pathways, designed to activate patients into self-management and support positive lifestyle change. Those with greater needs can be directed towards appropriate external providers, where many examples of innovative practice exist. These changes in policy and vision for the NHS present an opportunity for Integrative Oncology to develop further and to reach populations who would, in many other countries, remain underserved or hard-to-reach by whole person approaches

Using a whole person approach to support people with cancer: a longitudinal, mixed methods service evaluation

Introduction: Improved models of care are needed to meet all the support needs of people with cancer, which encompass psychological, emotional, physical, spiritual, sexual, occupational, social and existential needs. The aim of this paper is to (1) evaluate short and long-term impacts of using a whole person approach to support people with cancer on the Living Well with the Impact of Cancer Course (LWC); (2) use these data to inform strategic decisions about future service provision at Penny Brohn UK. Methods: Longitudinal mixed-methods service evaluation (n=135). Data collected included health related quality of life (HRQoL) (FACIT-SpEx); Concerns (types and severity - MYCaW); lifestyle behaviour (bespoke questionnaire) and participants’ experiences over 12 months post course. Results: Statistically and clinically significant improvements from baseline - 12 months in severity of MYCaW Concerns (n=64; p<0.000) and mean total HRQoL (n=66; p<0.000). The majority of MYCaW concerns were ‘psychological and emotional’ and about participants’ wellbeing. Spiritual, emotional and functional wellbeing contributed most to HRQoL improvements at 12 months. Barriers to maintaining healthy lifestyle changes included lack of support from family and friends, time constraints, and returning to work. 3-6 months post-course was identified as the time when more support was most likely to be needed. Conclusions: Using a whole person approach for the LWC enabled the needs of participants to be met, and statistically and clinically significant improvements in HRQoL and MYCaW Concerns were reported. Qualitative data analysis explored how experiencing whole person support enabled participants to make and sustain healthy lifestyle changes associated with improved survivorship. Barriers experienced to making health behaviour change were also identified. These data then informed wider and more person-centred clinical provision to increase the maintenance of positive long-term behaviour changes. Comparison of whole person approaches to cancer treatment and support and standard care are now urgently needed

Song variation of the South Eastern Indian Ocean pygmy blue whale population in the Perth Canyon, Western Australia

Sea noise collected over 2003 to 2017 from the Perth Canyon, Western Australia was analysed for variation in the South Eastern Indian Ocean pygmy blue whale song structure. The primary song-types were: P3, a three unit phrase (I, II and III) repeated with an inter-song interval (ISI) of 170–194 s; P2, a phrase consisting of only units II & III repeated every 84–96 s; and P1 with a phrase consisting of only unit II repeated every 45–49 s. The different ISI values were approximate multiples of each other within a season. When comparing data from each season, across seasons, the ISI value for each song increased significantly through time (all fits had p &lt; 0.001), at 0.30 s/Year (95%CI 0.217–0.383), 0.8 s/Year (95% CI 0.655–1.025) and 1.73 s/Year (95%CI 1.264–2.196) for the P1, P2 and P3 songs respectively. The proportions of each song-type averaged at 21.5, 24.2 and 56% for P1, P2 and P3 occurrence respectively and these ratios could vary by up to ± 8% (95% CI) amongst years. On some occasions animals changed the P3 ISI to be significantly shorter (120–160 s) or longer (220–280 s). Hybrid song patterns occurred where animals combined multiple phrase types into a repeated song. In recent years whales introduced further complexity by splitting song units. This variability of song-type and proportions implies abundance measure for this whale sub population based on song detection needs to factor in trends in song variability to make data comparable between seasons. Further, such variability in song production by a sub population of pygmy blue whales raises questions as to the stability of the song types that are used to delineate populations. The high level of song variability may be driven by an increasing number of background whale callers creating ‘noise’ and so forcing animals to alter song in order to ‘stand out’ amongst the crowd

Damage and repair classification in reinforced concrete beams using frequency domain data

This research aims at developing a new vibration-based damage classification technique that can efficiently be applied to a real-time large data. Statistical pattern recognition paradigm is relevant to perform a reliable site-location damage diagnosis system. By adopting such paradigm, the finite element and other inverse models with their intensive computations, corrections and inherent inaccuracies can be avoided. In this research, a two-stage combination between principal component analysis and Karhunen-Loéve transformation (also known as canonical correlation analysis) was proposed as a statistical-based damage classification technique. Vibration measurements from frequency domain were tested as possible damage-sensitive features. The performance of the proposed system was tested and verified on real vibration measurements collected from five laboratory-scale reinforced concrete beams modelled with various ranges of defects. The results of the system helped in distinguishing between normal and damaged patterns in structural vibration data. Most importantly, the system further dissected reasonably each main damage group into subgroups according to their severity of damage. Its efficiency was conclusively proved on data from both frequency response functions and response-only functions. The outcomes of this two-stage system showed a realistic detection and classification and outperform results from the principal component analysis-only. The success of this classification model is substantially tenable because the observed clusters come from well-controlled and known state conditions

Microfactory – Blend – Compression - Performance test

Aims and objectives - API’s with acicular habits are commonplace and present processing and handling challenges due to poor flow. This is traditionally addressed by wet granulation processes during formulation. Currently continuous direct compression (CDC) is gaining favour as a simplified formulation and dose formation process. However, poor flow properties limit CDC. This work aims to enable CDC by spherical agglomeration in the primary process and develop underpinning modelling approaches to allow formulations to be explored in-silico (i.e. digital twin) - Here at CMAC an integrated crystalisation-spherical agglomeration-drying-blending-compression process is being developed (microfactory) to be used to parameterise and develop modelling tools on the g-formulate package - This work presents some of the activities on the compression component to parameterise and develop a suitable model to enable the process to be explored (i.e. digital twin