Non-destructive assay of nuclear waste containers using muon scattering tomography in the Horizon2020 CHANCE project

Methods for the non-destructive assay of nuclear waste drums are of great importance to the nuclear waste management community, especially where loss in continuity of knowledge about the content of drums happened or chemical processes altering the contents of the drums may occur. Muon scattering tomography has been shown to be a promising technique for the non-destructive assay of nuclear waste drums in a safe way. By measuring tracks of muons entering and leaving the probed sample and extracting scattering angles from the tracks, it is possible to draw conclusions about the contents of the sample and its spatial arrangement. Within the CHANCE project, a newly built large-scale mobile detector system for scanning and imaging the contents of nuclear waste drums using atmospheric muons is currently undergoing commissioning

Physics-Informed Echo State Networks for Chaotic Systems Forecasting

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems

Physics-Informed Echo State Networks for Chaotic Systems Forecasting

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems.Comment: 7 pages, 3 figure

Sheep Updates 2005 - Part 2

This session covers seven papers from different authors: CONCURRENT SESSIONS - STRATEGIC MANAGEMENT 1.Finishing Pastoral Lambs, Peter Tozer, Patricia Harper, Janette Drew, Department of Agriculture Western Australia 2. Coating Improves Wool Quality under Mixed Farming Conditions, KE Kemper, ML Hebart, FD Brien, KS Grimson, DH Smith AMM Ramsay, South Australian Research and Development Institute 3. J. S. Richards, K.D. Atkins, T. Thompson, W. K. Murray, Australian Sheep Industry Co-operative Research Centre and NSW Department of Primary Industries, Orange Agricultural Institute, Forest Rd. Orange 4. Strategic Risk Management in the Sheep Industry, J.R.L. Hall, ICON Agriculture (JRL Hall & Co) 5. Joining Prime Lambs for the Northern End of the Market - a Systems Approach, Chris Carter, Peter Tozer, Department of Agriculture Western Australia 6. Lifetime Wool - Dry feed budgeting tool, Mike Hyder, department of Agriculture Western Australia, John Young, Farming Systems Analysis Service, Kojonup, Western Australia 7. Influence of ultrafine wool fibre curvature and blending with cashmere on attributes of knitwear, B. A. McGregor, Primary Industries Research Victoria, Department of Primary Industries, Victori

Sheep Updates 2005 - Part 4

This session covers twelve papers from different authors: REPRODUCTION 1. Is it worth increasing investment to increase lambing percentages? Lucy Anderton Department of Agriculture Western Australia. 2. What value is a lamb? John Young, Farming Systems Analysis Service, Kojonup, WA 3. Providing twin-bearing ewes with extra energy at lambing produces heavier lambs at marking. Rob Davidson WAMMCO International,, formerly University of Western Australia; Keith Croker, Ken Hart, Department of Agriculture Western Australia, Tim Wiese, Chuckem , Highbury, Western Australia. GENETICS 4. Underlying biological cause of trade-off between meat and wool. Part 1. Wool and muscle glycogen, BM Thomson, I Williams, University of WA, Crawley, JRBriegel, CSIRO Livestock Industries, Floreat Park WA &CRC for the Australian Sheep Industry, JC Greeff, Department of Agriculture Western Australia &CRC for the Australian Sheep Industry. 5. Underlying biological cause of trade-off between meat and wool. Part 2. Wool and fatness, NR Adams1,3, EN Bermingham1,3, JR Briegel1,3, JC Greeff2,3 1CSIRO Livestock Industries, Floreat Park WA 2Department of Agriculture Western Australia, 3CRC for the Australian Sheep Industry 6. Genetic trade-offs between lamb and wool production in Merino breeding programs, Johan Greeff, Department of Agriculture, Western Australia. 7. Clean fleece weight is no phenotypically independent of other traits. Sue Hatcherac and Gordon Refshaugebc aNSWDPI Orange Agricultural Institute, Orange NSW 2800 bUNE c/- NSWDPI Cowra AR&AS Cowra NSW 2794 cAustralian Sheep Industry CRC. 8. When you\u27re on a good thing, do it better: An economic analysis of sheep breed profitability. Emma Kopke, Ross Kingwell, Department of Agriculture, Western Australia, John Young, Farming Systems Analysis Service, Kojonup, WA. 9. Selection Demonstration Flocks: Demonstrating improvementsin productivity of merinos, K.E. Kemper, M.L. Hebart, F.D. Brien, K.S. Jaensch, R.J. Grimson, D.H. Smith South Australian Research and Development Institute 10. You are compromising yield by using Dust Penetration and GFW in breeding programs, Melanie Dowling, Department of Agriculture, Western Australia, A. (Tony) Schlink, CSIRO Livestock Industries, Wembley, Johan Greeff, Department of Agriculture Western Australia. 11. Merino Sheep can be bred for resistance to breech strike. Johan Greeff , John Karlsson, Department of Agriculture Western Australia 12. Parasite resistant sheep and hypersensitivity diarrhoea, L.J.E. Karlsson & J.C. Greeff, Department of Agriculture Western Australi

Power Versus Affiliation in Political Ideology

Posited motivational differences between liberals and conservatives have historically been controversial. This motivational interface has recently been bridged, but the vast majority of studies have used self-reports of values or motivation. Instead, the present four studies investigated whether two classic social motive themes—power and affiliation—vary by political ideology in objective linguistic analysis terms. Study 1 found that posts to liberal chat rooms scored higher in standardized affiliation than power, whereas the reverse was true of posts to conservative chat rooms. Study 2 replicated this pattern in the context of materials posted to liberal versus conservative political news websites. Studies 3 and 4, finally, replicated a similar interactive (ideology by motive type) pattern in State of the State and State of the Union addresses. Differences in political ideology, these results suggest, are marked by, and likely reflective of, mind-sets favoring affiliation (liberal) or power (conservative). </jats:p

A New Method to Address Unmet Needs for Extracting Individual Cell Migration Features from a Large Number of Cells Embedded in 3D Volumes

Background: In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule-induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases. Methodology/Principal Findings: In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the method results from detecting and managing the cases where two cell (mean-shift) trackers converge to the same point. The resulting trajectories quantify cell migration through statistical analysis of 3D trajectory descriptors. We manually validated the method and observed efficient cell detection and a low tracking error rate (6%). We also applied the method in a real biological experiment where the pro-migratory effects of hyaluronic acid (HA) were analyzed on brain cancer cells. Using collagen gels with increased HA proportions, we were able to evidence a dose-response effect on cell migration abilities. Conclusions/Significance: The developed method enables biomedical researchers to automatically and robustly quantify the pro- or anti-migratory effects of different experimental conditions on unlabeled cell cultures in a 3D environment. © 2011 Adanja et al.SCOPUS: ar.jinfo:eu-repo/semantics/publishe