An application of the requirement vs capability analysis to estimating design reliability of solid rocket motors

Design reliability parameters for solid propellant rocket engine

Self-Updating Models with Error Remediation

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.Comment: 17 pages, 13 figures, published in the proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II conference in the SPIE Defense + Commercial Sensing, 2020 symposiu

International Evidence on the Impact of Health-Justice Partnerships: A Systematic Scoping Review

BACKGROUND: Health-justice partnerships (HJPs) are collaborations between healthcare and legal services which support patients with social welfare issues such as welfare benefits, debt, housing, education and employment. HJPs exist across the world in a variety of forms and with diverse objectives. This review synthesizes the international evidence on the impacts of HJPs. METHODS: A systematic scoping review of international literature was undertaken. A wide-ranging search was conducted across academic databases and grey literature sources, covering OECD countries from January 1995 to December 2018. Data from included publications were extracted and research quality was assessed. A narrative synthesis approach was used to analyze and present the results. RESULTS: Reported objectives of HJPs related to: prevention of health and legal problems; access to legal assistance; health improvement; resolution of legal problems; improvement of patient care; support for healthcare services; addressing inequalities; and catalyzing systemic change. There is strong evidence that HJPs: improve access to legal assistance for people at risk of social and health disadvantage; positively influence material and social circumstances through resolution of legal problems; and improve mental wellbeing. A wide range of other positive impacts were identified for individuals, services and communities; the strength of evidence for each is summarized and discussed. CONCLUSION: HJPs are effective in tackling social welfare issues that affect the health of disadvantaged groups in society and can therefore form a key part of public health strategies to address inequalities

A NICER Discovery of a Low-Frequency Quasi-Periodic Oscillation in the Soft-Intermediate State of MAXI J1535-571

We present the discovery of a low-frequency $\approx 5.7$ Hz quasi-periodic oscillation (QPO) feature in observations of the black hole X-ray binary MAXI J1535-571 in its soft-intermediate state, obtained in September-October 2017 by the Neutron Star Interior Composition Explorer (NICER). The feature is relatively broad (compared to other low-frequency QPOs; quality factor $Q\approx 2$) and weak (1.9% rms in 3-10 keV), and is accompanied by a weak harmonic and low-amplitude broadband noise. These characteristics identify it as a weak Type A/B QPO, similar to ones previously identified in the soft-intermediate state of the transient black hole X-ray binary XTE J1550-564. The lag-energy spectrum of the QPO shows increasing soft lags towards lower energies, approaching 50 ms at 1 keV (with respect to a 3-10 keV continuum). This large phase shift has similar amplitude but opposite sign to that seen in Rossi X-ray Timing Explorer data for a Type B QPO from the transient black hole X-ray binary GX 339-4. Previous phase-resolved spectroscopy analysis of the Type B QPO in GX 339-4 pointed towards a precessing jet-like corona illuminating the accretion disk as the origin of the QPO signal. We suggest that this QPO in MAXI J1535-571 may have the same origin, with the different lag sign depending on the scale height of the emitting region and the observer inclination angle.Comment: Accepted for publication in ApJ Letter

Matrix controlled channel diffusion of sodium in amorphous silica

To find the origin of the diffusion channels observed in sodium-silicate glasses, we have performed classical molecular dynamics simulations of Na$_2$O--4SiO$_2$ during which the mass of the Si and O atoms has been multiplied by a tuning coefficient. We observe that the channels disappear and that the diffusive motion of the sodium atoms vanishes if this coefficient is larger than a threshold value. Above this threshold the vibrational states of the matrix are not compatible with those of the sodium ions. We interpret hence the decrease of the diffusion by the absence of resonance conditions.Comment: 5 pages, 4 figure