942 research outputs found

    Timber gridshells: beyond the drawing board

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    In March 2011, a week-long workshop that invited participation from all architecture and architectural technology students at Sheffield Hallam University, UK was organised with the objective of enhancing students’ thinking and experience by construction thinking. It was aimed at creating a sense of realness to realise a design project collectively. Timber was set as the material of exploration. The students had to make use of bending to design and create a timber gridshell structure. This made use of a quality traditionally felt to be a structural weakness of the material. To do this, students form-found non-mathematically and non-digitally using paper gridmats. This paper describes the aims, activity and outcome of the timber gridshell workshop as a way of preparing architects and technologists of the future and introducing the challenges of architectural design in terms of economics and construction process, aesthetics, effective communication and structural intuition by working with a given material – all important aspects in achieving effective architecture

    Nernst branes in gauged supergravity

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    We study static black brane solutions in the context of N = 2 U(1) gauged supergravity in four dimensions. Using the formalism of first-order flow equations, we construct novel extremal black brane solutions including examples of Nernst branes, i.e. extremal black brane solutions with vanishing entropy density. We also discuss a class of non-extremal generalizations which is captured by the first-order formalism.Comment: 44 pages, 3 figures, v2: added appendix B and references, minor typographic changes, v3: added some clarifying remarks, version published in JHE

    Lignin biomarkers as tracers of mercury sources in lakes water column

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    This study presents the role of specific terrigenous organic compounds as important vectors of mercury (Hg) transported from watersheds to lakes of the Canadian boreal forest. In order to differentiate the autochthonous from the allochthonous organic matter (OM), lignin derived biomarker signatures [Lambda, S/V, C/V, P/(V ? S), 3,5-Bd/V and (Ad/Al)v] were used. Since lignin is exclusively produced by terrigenous plants, this approach can give a non equivocal picture of the watershed inputs to the lakes. Moreover, it allows a characterization of the source of OM and its state of degradation. The water column of six lakes from the Canadian Shield was sampled monthly between June and September 2005. Lake total dissolved Hg concentrations and Lambda were positively correlated, meaning that Hg and ligneous inputs are linked (dissolved OM r2 = 0.62, p\0.0001; particulate OM r2 = 0.76, p\0.0001). Ratios of P/(V ? S) and 3,5-Bd/V from both dissolved OM and particulate OM of the water column suggest an inverse relationship between the progressive state of pedogenesis and maturation of the OM in soil before entering the lake, and the Hg concentrations in the water column. No relation was found between Hg levels in the lakes and the watershed flora composition—angiosperm versus gymnosperm or woody versus non-woody compounds. This study has significant implications for watershed management of ecosystems since limiting fresh terrestrial OM inputs should reduce Hg inputs to the aquatic systems. This is particularly the case for largescale land-use impacts, such as deforestation, agriculture and urbanization, associated to large quantities of soil OM being transferred to aquatic systems

    X-Ray Spectroscopy of Stars

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    (abridged) Non-degenerate stars of essentially all spectral classes are soft X-ray sources. Low-mass stars on the cooler part of the main sequence and their pre-main sequence predecessors define the dominant stellar population in the galaxy by number. Their X-ray spectra are reminiscent, in the broadest sense, of X-ray spectra from the solar corona. X-ray emission from cool stars is indeed ascribed to magnetically trapped hot gas analogous to the solar coronal plasma. Coronal structure, its thermal stratification and geometric extent can be interpreted based on various spectral diagnostics. New features have been identified in pre-main sequence stars; some of these may be related to accretion shocks on the stellar surface, fluorescence on circumstellar disks due to X-ray irradiation, or shock heating in stellar outflows. Massive, hot stars clearly dominate the interaction with the galactic interstellar medium: they are the main sources of ionizing radiation, mechanical energy and chemical enrichment in galaxies. High-energy emission permits to probe some of the most important processes at work in these stars, and put constraints on their most peculiar feature: the stellar wind. Here, we review recent advances in our understanding of cool and hot stars through the study of X-ray spectra, in particular high-resolution spectra now available from XMM-Newton and Chandra. We address issues related to coronal structure, flares, the composition of coronal plasma, X-ray production in accretion streams and outflows, X-rays from single OB-type stars, massive binaries, magnetic hot objects and evolved WR stars.Comment: accepted for Astron. Astrophys. Rev., 98 journal pages, 30 figures (partly multiple); some corrections made after proof stag

    New prioritized value iteration for Markov decision processes

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    The problem of solving large Markov decision processes accurately and quickly is challenging. Since the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose an improved value iteration algorithm based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach. © Springer Science+Business Media B.V. 2011.García Hernández, MDG.; Ruiz Pinales, J.; Onaindia De La Rivaherrera, E.; Aviña Cervantes, JG.; Ledesma Orozco, S.; Alvarado Mendez, E.; Reyes Ballesteros, A. (2012). New prioritized value iteration for Markov decision processes. Artificial Intelligence Review. 37(2):157-167. doi:10.1007/s10462-011-9224-zS157167372Agrawal S, Roth D (2002) Learning a sparse representation for object detection. In: Proceedings of the 7th European conference on computer vision. Copenhagen, Denmark, pp 1–15Bellman RE (1954) The theory of dynamic programming. Bull Amer Math Soc 60: 503–516Bellman RE (1957) Dynamic programming. Princeton University Press, New JerseyBertsekas DP (1995) Dynamic programming and optimal control. Athena Scientific, MassachusettsBhuma K, Goldsmith J (2003) Bidirectional LAO* algorithm. 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Cumbria, UKBoutilier C, Dean T, Hanks S (1999) Decision-theoretic planning: structural assumptions and computational leverage. J Artif Intell Res 11: 1–94Chang I, Soo H (2007) Simulation-based algorithms for Markov decision processes Communications and control engineering. Springer, LondonDai P, Goldsmith J (2007a) Faster dynamic programming for Markov decision processes. Technical report. Doctoral consortium, department of computer science and engineering. University of WashingtonDai P, Goldsmith J (2007b) Topological value iteration algorithm for Markov decision processes. In: Proceedings of the 20th international joint conference on artificial intelligence. Hyderabad, India, pp 1860–1865Dai P, Hansen EA (2007c) Prioritizing bellman backups without a priority queue. In: Proceedings of the 17th international conference on automated planning and scheduling, association for the advancement of artificial intelligence. Rhode Island, USA, pp 113–119Dibangoye JS, Chaib-draa B, Mouaddib A (2008) A Novel prioritization technique for solving Markov decision processes. In: Proceedings of the 21st international FLAIRS (The Florida Artificial Intelligence Research Society) conference, association for the advancement of artificial intelligence. Florida, USAFerguson D, Stentz A (2004) Focused propagation of MDPs for path planning. In: Proceedings of the 16th IEEE international conference on tools with artificial intelligence. pp 310–317Hansen EA, Zilberstein S (2001) LAO: a heuristic search algorithm that finds solutions with loops. Artif Intell 129: 35–62Hinderer K, Waldmann KH (2003) The critical discount factor for finite Markovian decision processes with an absorbing set. Math Methods Oper Res 57: 1–19Li L (2009) A unifying framework for computational reinforcement learning theory. PhD Thesis. The state university of New Jersey, New Brunswick. NJLittman ML, Dean TL, Kaelbling LP (1995) On the complexity of solving Markov decision problems.In: Proceedings of the 11th international conference on uncertainty in artificial intelligence. Montreal, Quebec pp 394–402McMahan HB, Gordon G (2005a) Fast exact planning in Markov decision processes. In: Proceedings of the 15th international conference on automated planning and scheduling. Monterey, CA, USAMcMahan HB, Gordon G (2005b) Generalizing Dijkstra’s algorithm and gaussian elimination for solving MDPs. Technical report, Carnegie Mellon University, PittsburghMeuleau N, Brafman R, Benazera E (2006) Stochastic over-subscription planning using hierarchies of MDPs. In: Proceedings of the 16th international conference on automated planning and scheduling. Cumbria, UK, pp 121–130Moore A, Atkeson C (1993) Prioritized sweeping: reinforcement learning with less data and less real time. Mach Learn 13: 103–130Puterman ML (1994) Markov decision processes. 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Springer, New YorkWingate D, Seppi KD (2005) Prioritization methods for accelerating MDP solvers. J Mach Learn Res 6: 851–88

    Understanding pharmacokinetics using realistic computational models of fluid dynamics: biosimulation of drug distribution within the CSF space for intrathecal drugs

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    We introduce how biophysical modeling in pharmaceutical research and development, combining physiological observations at the tissue, organ and system level with selected drug physiochemical properties, may contribute to a greater and non-intuitive understanding of drug pharmacokinetics and therapeutic design. Based on rich first-principle knowledge combined with experimental data at both conception and calibration stages, and leveraging our insights on disease processes and drug pharmacology, biophysical modeling may provide a novel and unique opportunity to interactively characterize detailed drug transport, distribution, and subsequent therapeutic effects. This innovative approach is exemplified through a three-dimensional (3D) computational fluid dynamics model of the spinal canal motivated by questions arising during pharmaceutical development of one molecular therapy for spinal cord injury. The model was based on actual geometry reconstructed from magnetic resonance imaging data subsequently transformed in a parametric 3D geometry and a corresponding finite-volume representation. With dynamics controlled by transient Navier–Stokes equations, the model was implemented in a commercial multi-physics software environment established in the automotive and aerospace industries. While predictions were performed in silico, the underlying biophysical models relied on multiple sources of experimental data and knowledge from scientific literature. The results have provided insights into the primary factors that can influence the intrathecal distribution of drug after lumbar administration. This example illustrates how the approach connects the causal chain underlying drug distribution, starting with the technical aspect of drug delivery systems, through physiology-driven drug transport, then eventually linking to tissue penetration, binding, residence, and ultimately clearance. Currently supporting our drug development projects with an improved understanding of systems physiology, biophysical models are being increasingly used to characterize drug transport and distribution in human tissues where pharmacokinetic measurements are difficult or impossible to perform. Importantly, biophysical models can describe emergent properties of a system, i.e. properties not identifiable through the study of the system’s components taken in isolation

    Measurement of the branching fraction and CP content for the decay B(0) -> D(*+)D(*-)

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    This is the pre-print version of the Article. The official published version can be accessed from the links below. Copyright @ 2002 APS.We report a measurement of the branching fraction of the decay B0→D*+D*- and of the CP-odd component of its final state using the BABAR detector. With data corresponding to an integrated luminosity of 20.4  fb-1 collected at the Υ(4S) resonance during 1999–2000, we have reconstructed 38 candidate signal events in the mode B0→D*+D*- with an estimated background of 6.2±0.5 events. From these events, we determine the branching fraction to be B(B0→D*+D*-)=[8.3±1.6(stat)±1.2(syst)]×10-4. The measured CP-odd fraction of the final state is 0.22±0.18(stat)±0.03(syst).This work is supported by DOE and NSF (USA), NSERC (Canada), IHEP (China), CEA and CNRS-IN2P3 (France), BMBF (Germany), INFN (Italy), NFR (Norway), MIST (Russia), and PPARC (United Kingdom). Individuals have received support from the A.P. Sloan Foundation, Research Corporation, and Alexander von Humboldt Foundation

    Measurement of D-s(+) and D-s(*+) production in B meson decays and from continuum e(+)e(-) annihilation at √s=10.6 GeV

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    This is the pre-print version of the Article. The official published version can be accessed from the links below. Copyright @ 2002 APSNew measurements of Ds+ and Ds*+ meson production rates from B decays and from qq̅ continuum events near the Υ(4S) resonance are presented. Using 20.8 fb-1 of data on the Υ(4S) resonance and 2.6 fb-1 off-resonance, we find the inclusive branching fractions B(B⃗Ds+X)=(10.93±0.19±0.58±2.73)% and B(B⃗Ds*+X)=(7.9±0.8±0.7±2.0)%, where the first error is statistical, the second is systematic, and the third is due to the Ds+→φπ+ branching fraction uncertainty. The production cross sections σ(e+e-→Ds+X)×B(Ds+→φπ+)=7.55±0.20±0.34pb and σ(e+e-→Ds*±X)×B(Ds+→φπ+)=5.8±0.7±0.5pb are measured at center-of-mass energies about 40 MeV below the Υ(4S) mass. The branching fractions ΣB(B⃗Ds(*)+D(*))=(5.07±0.14±0.30±1.27)% and ΣB(B⃗Ds*+D(*))=(4.1±0.2±0.4±1.0)% are determined from the Ds(*)+ momentum spectra. The mass difference m(Ds+)-m(D+)=98.4±0.1±0.3MeV/c2 is also measured.This work was supported by DOE and NSF (USA), NSERC (Canada), IHEP (China), CEA and CNRS-IN2P3 (France), BMBF (Germany), INFN (Italy), NFR (Norway), MIST (Russia), and PPARC (United Kingdom). Individuals have received support from the Swiss NSF, A. P. Sloan Foundation, Research Corporation, and Alexander von Humboldt Foundation
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