Hybrid mean field and real space model for vacancy diffusion-mediated annealing of radiation defects

In a fusion or advanced fission reactor, high energy neutrons induce the formation of extended defect clusters in structural component materials, degrading their properties over time. Such damage can be partially recovered via a thermal annealing treatment. Therefore, for the design and operation of fusion and advanced fission nuclear energy systems it is critical to estimate and predict the annealing timescales for arbitrary configurations of defect clusters. In our earlier paper [I. Rovelli, S. L. Dudarev, and A. P. Sutton, J. Mech. Phys. Solids 103, 121 (2017)] we extended the Green function formulation by Gu, Xiang et al. [Y. Gu, Y. Xiang, S. S. Quek, and D. J. Srolovitz, J. Mech. Phys. Solids 83, 319 (2015)] for the climb of curved dislocations, to include the evaporation and growth of cavities and vacancy clusters, and take into account the effect of free surfaces. In this work, we further develop this model to include the effect of radiation defects that are below the experimental detection limit, via a mean field approach coupled with an explicit treatment of the evolution of discrete defect clusters distributed in real space. We show that randomly distributed small defects screen diffusive interactions between larger discrete clusters. The evolution of the coupled system is modelled self-consistently. We also simulate the evolution of defects in an infinite laterally extended thin film, using the Ewald summation of screened Yukawa-type diffusive propagators

Theory and Simulation of the diffusion of kinks on dislocations in bcc metals

Isolated kinks on thermally fluctuating (1/2) screw, edge and (1/2) edge dislocations in bcc iron are simulated under zero stress conditions using molecular dynamics (MD). Kinks are seen to perform stochastic motion in a potential landscape that depends on the dislocation character and geometry, and their motion provides fresh insight into the coupling of dislocations to a heat bath. The kink formation energy, migration barrier and friction parameter are deduced from the simulations. A discrete Frenkel-Kontorova-Langevin (FKL) model is able to reproduce the coarse grained data from MD at a fraction of the computational cost, without assuming an a priori temperature dependence beyond the fluctuation-dissipation theorem. Analytic results reveal that discreteness effects play an essential r\^ole in thermally activated dislocation glide, revealing the existence of a crucial intermediate length scale between molecular and dislocation dynamics. The model is used to investigate dislocation motion under the vanishingly small stress levels found in the evolution of dislocation microstructures in irradiated materials

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Development of an analysis for the determination of coupled helicopter rotor/control system dynamic response. Part 1: Analysis and applications

A theoretical analysis is developed for a coupled helicopter rotor system to allow determination of the loads and dynamic response behavior of helicopter rotor systems in both steady-state forward flight and maneuvers. The effects of an anisotropically supported swashplate or gyroscope control system and a deformed free wake on the rotor system dynamic response behavior are included in the analysis

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events

Electromagnetic (EM) observations of gravitational-wave (GW) sources would bring unique insights into a source which are not available from either channel alone. However EM follow-up of GW events presents new challenges. GW events will have large sky error regions, on the order of 10-100 square degrees, which can be made up of many disjoint patches. When searching such large areas there is potential contamination by EM transients unrelated to the GW event. Furthermore, the characteristics of possible EM counterparts to GW events are also uncertain. It is therefore desirable to be able to assess the statistical significance of a candidate EM counterpart, which can only be done by performing background studies of large data sets. Current image processing pipelines such as that used by ROTSE are not usually optimised for large-scale processing. We have automated the ROTSE image analysis, and supplemented it with a post-processing unit for candidate validation and classification. We also propose a simple ad hoc statistic for ranking candidates as more likely to be associated with the GW trigger. We demonstrate the performance of the automated pipeline and ranking statistic using archival ROTSE data. EM candidates from a randomly selected set of images are compared to a background estimated from the analysis of 102 additional sets of archival images. The pipeline's detection efficiency is computed empirically by re-analysis of the images after adding simulated optical transients that follow typical light curves for gamma-ray burst afterglows and kilonovae. We show that the automated pipeline rejects most background events and is sensitive to simulated transients to limiting magnitudes consistent with the limiting magnitude of the images

The contribution of organisational factors to vicarious trauma in mental health professionals: a systematic review and narrative synthesis

Background: The negative impact of trauma work has been well documented in mental health professionals. There are three main phenomena used to describe these effects: Secondary Traumatic Stress (STS), Vicarious Trauma (VT) and Compassion Fatigue (CF). To date, the majority of research has focused on the contribution of individual level factors. However, it is imperative to also understand the role of organizational factors. Objectives: This review examines the role of organizational factors in ameliorating or preventing STS, VT, and CF in mental health professionals. We further aimed to identify specific elements of these factors which are perceived to be beneficial and/or detrimental in mitigating against the effects of STS, VT, and CF. Method: Studies were identified by searching the electronic databases Medline, PsycINFO, Embase, Web of Science and SCOPUS with final searches taking place on 10 March 2021. Results: Twenty-three quantitative studies, eight qualitative studies, and five mixed methods studies were included in the final review. A narrative synthesis was conducted to analyse the findings. The results of the review highlight the importance of regular supervision within supportive supervisory relationships, strong peer support networks, and balanced and diverse caseloads. The value of having an organizational culture which acknowledges and validates the existence of STS was also imperative. Conclusions: Organizations have an ethical responsibility to support the mental health professionals they employ and provide a supportive environment which protects them against STS. This review provides preliminary evidence for the types of support that should be offered and highlights the gaps in the literature and where future research should be directed. Further research is needed to evaluate which strategies–and under what conditions–best ameliorate and prevent STS