Semantic segmentation of explosive volcanic plumes through deep learning

Tracking explosive volcanic phenomena can provide important information for hazard monitoring and volcano research. Perhaps the simplest forms of monitoring instruments are visible-wavelength cameras, which are routinely deployed on volcanoes around the globe. Here, we present the development of deep learning models, based on convolutional neural networks (CNNs), to perform semantic segmentation of explosive volcanic plumes on visible imagery, therefore classifying each pixel of an image as either explosive plume or not explosive plume. We have developed 3 models, each with average validation accuracies of >97% under 10-fold cross-validation; although we do highlight that, due to the limited training and validation dataset, this value is likely an overestimate of real-world performance. We then present model deployment for automated retrieval of plume height, rise speed and propagation direction, all parameters which can have great utility particularly in ash dispersion modelling and associated aviation hazard identification. The 3 trained models are freely available for download at https://doi.org/10.15131/shef.data.17061509

Ultraviolet camera measurements of passive and explosive (Strombolian) sulphur dioxide emissions at Yasur Volcano, Vanuatu

Here, we present the first ultraviolet (UV) camera measurements of sulphur dioxide (SO2) flux from Yasur volcano, Vanuatu, for the period 6–9 July 2018. These data yield the first direct gas-measurement-derived calculations of explosion gas masses at Yasur. Yasur typically exhibits persistent passive gas release interspersed with frequent Strombolian explosions. We used compact forms of the “PiCam” Raspberry Pi UV camera system powered through solar panels to collect images. Our daily median SO2 fluxes ranged from 4 to 5.1 kg s−1, with a measurement uncertainty of −12.2% to +14.7%, including errors from the gas cell calibration drift, uncertainties in plume direction and distance, and errors from the plume velocity. This work highlights the use of particle image velocimetry (PIV) for plume velocity determination, which was preferred over the typically used cross-correlation and optical flow methods because of the ability to function over a variety of plume conditions. We calculated SO2 masses for Strombolian explosions ranging 8–81 kg (mean of 32 kg), which to our knowledge is the first budget of explosive gas masses from this target. Through the use of a simple statistical measure using the moving minimum, we estimated that passive degassing is the dominant mode of gas emission at Yasur, supplying an average of ~69% of the total gas released. Our work further highlights the utility of UV camera measurements in volcanology, and particularly the benefit of the multiple camera approach in error characterisation. This work also adds to our inventory of gas-based data, which can be used to characterise the spectrum of Strombolian activity across the globe.</jats:p

Mental health training programmes for non-mental health trained professionals coming into contact with people with mental ill health: a systematic review of effectiveness

Background The police and others in occupations where they come into close contact with people experiencing/with mental ill health, often have to manage difficult and complex situations. Training is needed to equip them to recognise and assist when someone has a mental health issue or learning/intellectual disability. We undertook a systematic review of the effectiveness of training programmes aimed at increasing knowledge, changing behaviour and/or attitudes of the trainees with regard to mental ill health, mental vulnerability, and learning disabilities. Methods Databases searched from 1995 onwards included: ASSIA, Cochrane Central Register of Controlled Clinical Trials (CENTRAL), Criminal Justice Abstracts, Embase, ERIC, MEDLINE, PsycINFO, Social Science Citation Index. Courses, training, or learning packages aimed at helping police officers and others who interact with the public in a similar way to deal with people with mental health problems were included. Primary outcomes were change in practice and change in outcomes for the groups of people the trainees come into contact with. Systematic reviews, randomised controlled trials (RCTs) and non- randomised controlled trials (non-RCTs) were included and quality assessed. In addition non-comparative evaluations of training for police in England were included. Results From 8578 search results, 19 studies met the inclusion criteria: one systematic review, 12 RCTs, three prospective non-RCTs, and three non-comparative studies. The training interventions identified included broad mental health awareness training and packages addressing a variety of specific mental health issues or conditions. Trainees included police officers, teachers and other public sector workers. Some short term positive changes in behaviour were identified for trainees, but for the people the trainees came into contact with there was little or no evidence of benefit. Conclusions A variety of training programmes exist for non-mental health professionals who come into contact with people who have mental health issues. There may be some short term change in behaviour for the trainees, but longer term follow up is needed. Research evaluating training for UK police officers is needed in which a number of methodological issues need to be addressed

Genome-wide analysis of Sphingomonas wittichii RW1 behaviour during inoculation and growth in contaminated sand.

The efficacy of inoculation of single pure bacterial cultures into complex microbiomes, for example, in order to achieve increased pollutant degradation rates in contaminated material (that is, bioaugmentation), has been frustrated by insufficient knowledge on the behaviour of the inoculated bacteria under the specific abiotic and biotic boundary conditions. Here we present a comprehensive analysis of genome-wide gene expression of the bacterium Sphingomonas wittichii RW1 in contaminated non-sterile sand, compared with regular suspended batch growth in liquid culture. RW1 is a well-known bacterium capable of mineralizing dibenzodioxins and dibenzofurans. We tested the reactions of the cells both during the immediate transition phase from liquid culture to sand with or without dibenzofuran, as well as during growth and stationary phase in sand. Cells during transition show stationary phase characteristics, evidence for stress and for nutrient scavenging, and adjust their primary metabolism if they were not precultured on the same contaminant as found in the soil. Cells growing and surviving in sand degrade dibenzofuran but display a very different transcriptome signature as in liquid or in liquid culture exposed to chemicals inducing drought stress, and we obtain evidence for numerous 'soil-specific' expressed genes. Studies focusing on inoculation efficacy should test behaviour under conditions as closely as possible mimicking the intended microbiome conditions

Robot Assisted Training for the Upper Limb after Stroke (RATULS): study protocol for a randomised controlled trial.

BACKGROUND: Loss of arm function is a common and distressing consequence of stroke. We describe the protocol for a pragmatic, multicentre randomised controlled trial to determine whether robot-assisted training improves upper limb function following stroke. METHODS/DESIGN: Study design: a pragmatic, three-arm, multicentre randomised controlled trial, economic analysis and process evaluation. SETTING: NHS stroke services. PARTICIPANTS: adults with acute or chronic first-ever stroke (1 week to 5 years post stroke) causing moderate to severe upper limb functional limitation. Randomisation groups: 1. Robot-assisted training using the InMotion robotic gym system for 45 min, three times/week for 12 weeks 2. Enhanced upper limb therapy for 45 min, three times/week for 12 weeks 3. Usual NHS care in accordance with local clinical practice Randomisation: individual participant randomisation stratified by centre, time since stroke, and severity of upper limb impairment. PRIMARY OUTCOME: upper limb function measured by the Action Research Arm Test (ARAT) at 3 months post randomisation. SECONDARY OUTCOMES: upper limb impairment (Fugl-Meyer Test), activities of daily living (Barthel ADL Index), quality of life (Stroke Impact Scale, EQ-5D-5L), resource use, cost per quality-adjusted life year and adverse events, at 3 and 6 months. Blinding: outcomes are undertaken by blinded assessors. Economic analysis: micro-costing and economic evaluation of interventions compared to usual NHS care. A within-trial analysis, with an economic model will be used to extrapolate longer-term costs and outcomes. Process evaluation: semi-structured interviews with participants and professionals to seek their views and experiences of the rehabilitation that they have received or provided, and factors affecting the implementation of the trial. SAMPLE SIZE: allowing for 10% attrition, 720 participants provide 80% power to detect a 15% difference in successful outcome between each of the treatment pairs. Successful outcome definition: baseline ARAT 0-7 must improve by 3 or more points; baseline ARAT 8-13 improve by 4 or more points; baseline ARAT 14-19 improve by 5 or more points; baseline ARAT 20-39 improve by 6 or more points. DISCUSSION: The results from this trial will determine whether robot-assisted training improves upper limb function post stroke. TRIAL REGISTRATION: ISRCTN, identifier: ISRCTN69371850 . Registered 4 October 2013

T2K neutrino flux prediction

cited By 15 art_number: 012001 affiliation: Centre for Particle Physics, Department of Physics, University of Alberta, Edmonton, AB, Canada; Albert Einstein Center for Fundamental Physics, Laboratory for High Energy Physics (LHEP), University of Bern, Bern, Switzerland; Department of Physics, Boston University, Boston, MA, United States; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; Department of Physics and Astronomy, University of California Irvine, Irvine, CA, United States; IRFU, CEA Saclay, Gif-sur-Yvette, France; Institute for Universe and Elementary Particles, Chonnam National University, Gwangju, South Korea; Department of Physics, University of Colorado at Boulder, Boulder, CO, United States; Department of Physics, Colorado State University, Fort Collins, CO, United States; Department of Physics, Dongshin University, Naju, South Korea; Department of Physics, Duke University, Durham, NC, United States; IN2P3-CNRS, Laboratoire Leprince-Ringuet, Ecole Polytechnique, Palaiseau, France; Institute for Particle Physics, ETH Zurich, Zurich, Switzerland; Section de Physique, DPNC, University of Geneva, Geneva, Switzerland; H. Niewodniczanski Institute of Nuclear Physics PAN, Cracow, Poland; High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki, Japan; Institut de Fisica d’Altes Energies (IFAE), Bellaterra (Barcelona), Spain; IFIC (CSIC and University of Valencia), Valencia, Spain; Department of Physics, Imperial College London, London, United Kingdom; INFN Sezione di Bari, Dipartimento Interuniversitario di Fisica, Università e Politecnico di Bari, Bari, Italy; INFN Sezione di Napoli and Dipartimento di Fisica, Università di Napoli, Napoli, Italy; INFN Sezione di Padova, Dipartimento di Fisica, Università di Padova, Padova, Italy; INFN Sezione di Roma, Università di Roma la Sapienza, Roma, Italy; Institute for Nuclear Research, Russian Academy of Sciences, Moscow, Russian Federation; Kobe University, Kobe, Japan; Department of Physics, Kyoto University, Kyoto, Japan; Physics Department, Lancaster University, Lancaster, United Kingdom; Department of Physics, University of Liverpool, Liverpool, United Kingdom; Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States; Université de Lyon, Université Claude Bernard Lyon 1, IPN Lyon (IN2P3), Villeurbanne, France; Department of Physics, Miyagi University of Education, Sendai, Japan; National Centre for Nuclear Research, Warsaw, Poland; State University of New York at Stony Brook, Stony Brook, NY, United States; Department of Physics and Astronomy, Osaka City University, Department of Physics, Osaka, Japan; Department of Physics, Oxford University, Oxford, United Kingdom; UPMC, Université Paris Diderot, Laboratoire de Physique Nucléaire et de Hautes Energies (LPNHE), Paris, France; Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, United States; School of Physics, Queen Mary University of London, London, United Kingdom; Department of Physics, University of Regina, Regina, SK, Canada; Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States; III. Physikalisches Institut, RWTH Aachen University, Aachen, Germany; Department of Physics and Astronomy, Seoul National University, Seoul, South Korea; Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom; University of Silesia, Institute of Physics, Katowice, Poland; STFC, Rutherford Appleton Laboratory, Harwell Oxford, Warrington, United Kingdom; Department of Physics, University of Tokyo, Tokyo, Japan; Institute for Cosmic Ray Research, Kamioka Observatory, University of Tokyo, Kamioka, Japan; Institute for Cosmic Ray Research, Research Center for Cosmic Neutrinos, University of Tokyo, Kashiwa, Japan; Department of Physics, University of Toronto, Toronto, ON, Canada; TRIUMF, Vancouver, BC, Canada; Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada; Faculty of Physics, University of Warsaw, Warsaw, Poland; Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland; Department of Physics, University of Warwick, Coventry, United Kingdom; Department of Physics, University of Washington, Seattle, WA, United States; Department of Physics, University of Winnipeg, Winnipeg, MB, Canada; Faculty of Physics and Astronomy, Wroclaw University, Wroclaw, Poland; Department of Physics and Astronomy, York University, Toronto, ON, Canada references: Astier, P., (2003) Nucl. 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