11,689 research outputs found

    CJ Koh Professorial Lecture Series; 3

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    This third report in the series captures the rich and insightful discussions arising from Professor Ruth Hayhoe’s visit to NIE in her appointment as the 7th CJ Koh Professor from 30 April to 4 May 2012. A comparative scholar who specialises in the field of comparative education in China, Prof Hayhoe delivered two lectures while here. A roundtable symposium was also organised in conjunction with her visit, where peers from Finland, Korea, Hong Kong, the United States, and colleagues from NIE engaged in dialogue about how their countries’ education systems continually innovate to stay on the top of their league

    The vanishing author in computer-generated works: a critical analysis of recent Australian case law

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    Abstract The use of software is ubiquitous in the creation of many copyright works, yet the requirement in copyright law that every work have a human author who engages in independent intellectual effort means that its use may prevent copyright subsistence. Several recent Australian cases have refocused attention on authorship as an essential criterion of copyright subsistence, and these cases suggest that much computer-produced output may be authorless and thus lack copyright protection. This article, the first in a two-part series, analyses how each case deals with the question of authorship of computer-produced works and why the use of software diminishes copyright protection for a significant number of computer-generated works. The article critiques the application of conventional notions of human authorship developed in the pre-computer age to modern productions and suggests alternative approaches to authorship that satisfy both the major objectives of copyright policy and the need to adapt to the computer age. The article argues that, without a broader judicial approach to authorship of computer-generated works, Parliament must remedy the lacuna in protection for these ‘authorless’ works. Possible solutions for reform are suggested. In a forthcoming article, the author comprehensively examines those reform proposals

    Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal

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    The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If we take various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering specific systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, we believe that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief. In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this paper proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. We developed and verified an approximate probabilistic inference program for the specific Bayesian network using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a model for uncertainty propagation of relevant parameters were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, we could connect the results of probabilistic inference from the Bayesian network with the consequence evaluation model addressed. We got a number of practical results to improve current knowledge base for the prioritization of future risk-dominant variables in an actual site. (c) 2005 Published by Elsevier Ltd

    Observation of psi(3686) -> e(+)e(-)chi(cJ) and chi(cJ) -> e(+)e(-)J/psi

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    Kolcu, Onur Buğra (Arel Author)Using 4.479 x 10(8) psi(3686) events collected with the BESIII detector, we search for the decays psi(3686) -> e(+)e(-)chi(cJ) and chi(cJ) -> e(+)e(-)J/psi, where J = 0, 1, 2. The decays psi(3686) -> e(+)e(-)chi(cJ) and chi(cJ) -> e(+)e(-)J/psi are observed for the first time. The measured branching fractions are B(psi(3686) -> e(+)e(-)chi(cJ)) = (11.7 +/- 2.5 +/- 1.0) x 10(-4), (8.6 +/- 0.3 +/- 0.6) x 10(-4), (6.9 +/- 0.5 +/- 0.6) x 10(-4) for J = 0, 1, 2, and B(chi(cJ) -> e(+)e(-)J/psi) = (1.51 +/- 0.30 +/- 0.13)x10(-4), (3.73 +/- 0.09 +/- 0.25)x10(-3), (2.48 +/- 0.08 +/- 0.16)x10(-3) for J = 0, 1, 2, respectively. The ratios of the branching fractions B(psi(3686) -> e(+)e(-)chi(cJ))/B(psi(3686) -> gamma chi(cJ)) and B(chi(cJ) -> e(+)e(-)J/psi)/B(chi(cJ) -> gamma J/psi) are also reported. Also, the alpha values of helicity angular distributions of the e(+)e(-) pair are determined for psi(3686) -> e(+)e(-)chi(c1,2) and chi(c1,2) -> e(+)e(-)J/psi

    Growth of vertically aligned bamboo-shaped carbon nanotubes

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    The vertically aligned uniformed carbon nanotubes (CNTs) oil a large area of Ni deposited Si substrates were grown by thermal chemical vapor deposition using C2H2 gas. The diameter of CNTs is as small as about 60 nm and the length is about 50 mum. High-resolution TEM analysis reveals that the CNTs have the uniformed multi-walls, the bamboo structure, and the sharp closed tip. The CNTs have multi-walls with good crystallinity and there are some defects on the wall surface. The base growth model is suitable to bamboo-shaped CNTs using thermal chemical vapor deposition
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