Modified Korteweg-de Vries Hierachies in Multiple-Times Variables and the Solutions of Modified Boussinesq Equations

We study solitary-wave and kink-wave solutions of a modified Boussinesq equation through a multiple-time reductive perturbation method. We use appropriated modified Korteweg-de Vries hierarchies to eliminate secular producing terms in each order of the perturbative scheme. We show that the multiple-time variables needed to obtain a regular perturbative series are completely determined by the associated linear theory in the case of a solitary-wave solution, but requires the knowledge of each order of the perturbative series in the case of a kink-wave solution. These appropriate multiple-time variables allow us to show that the solitary-wave as well as the kink-wave solutions of the modified Botussinesq equation are actually respectively a solitary-wave and a kink-wave satisfying all the equations of suitable modified Korteweg-de Vries hierarchies.Comment: RevTex file, submitted to Proc. Roy. Soc. London

Reliability analysis of dynamic systems by translating temporal fault trees into Bayesian networks

Classical combinatorial fault trees can be used to assess combinations of failures but are unable to capture sequences of faults, which are important in complex dynamic systems. A number of proposed techniques extend fault tree analysis for dynamic systems. One of such technique, Pandora, introduces temporal gates to capture the sequencing of events and allows qualitative analysis of temporal fault trees. Pandora can be easily integrated in model-based design and analysis techniques. It is, therefore, useful to explore the possible avenues for quantitative analysis of Pandora temporal fault trees, and we identify Bayesian Networks as a possible framework for such analysis. We describe how Pandora fault trees can be translated to Bayesian Networks for dynamic dependability analysis and demonstrate the process on a simplified fuel system model. The conversion facilitates predictive reliability analysis of Pandora fault trees, but also opens the way for post-hoc diagnostic analysis of failures

The taxidermist's apprentice:Stitching together the past and present of a craft practice

How do you witness the development and reproduction of a craft practice? This essay explores this provocation in relation to the craft practice of taxidermy and, in so doing, aims to stitch together non-representational and historical geographic concerns within the discipline. Mobilising and developing on an Ingoldian perspective on the process of skill, the author places herself in the position of apprentice to a practising taxidermist in recognition that the position of learner is a highly instructive context in which to enquire into how present-day practice relates to a representational culture charting the development of the craft in historical ‘how-to-do’ manuals. When juxtaposing contemporary ethnographies of taxidermy practice with descriptions of practice in historical ‘how-to-do’ manuals, the author shows how past and present practice resonates rather than replicates. Overall, this article aims to introduce and develop theoretical and methodological pathways for studying and storying (historical) geographies of craft and skilled practices. </jats:p

An Economic Risk Analysis of No-till Management for the Rice-Soybean Rotation System used in Arkansas

Arkansas is the top domestic rice producer, representing nearly half of total U.S. rice production. Sediment is one of the major pollutants in rice producing areas of Arkansas. In order to mitigate this problem no-tillage management is often recommended. No-tillage is not well understood by farmers who believe that no-till is less profitable due to lower yields offsetting cost savings. This study evaluates the profitability and variability of no-till in the typical rice-soybean rotation used in Arkansas rice production. Crop yields, prices and prices for key production inputs (fuel and fertilizer) are simulated for the rotation, and net return distributions for rice, soybean and the two-year rotation are evaluated for no-till and conventional till using stochastic efficiency with respect to a function (SERF) analysis. The results indicate that both risk neutral and risk-averse rice producers would prefer no-till over conventional till management in the two year rice-soybean rotation, and that no-till soybeans contribute greatly to the overall profitability of the rotation.simulation, rice-soybean, no tillage-profitability, risk analysis, Environmental Economics and Policy, Farm Management, Resource /Energy Economics and Policy,

The Potassium abundance in the globular clusters NGC104, NGC6752 and NGC6809

We derived Potassium abundances in red giant branch stars in the Galactic globular clusters NGC104 (144 stars), NGC6752 (134 stars) and NGC6809 (151 stars) using high-resolution spectra collected with FLAMES at the ESO - Very Large Telescope. In the considered samples we do not find significant intrinsic spreads in [K/Fe] (confirming the previous findings by Carretta et al.), at variance with the cases of the massive clusters NGC2419 and NGC2808. Additionally, marginally significant [K/Fe]-[O/Fe] anti-correlations are found in NGC104 and NGC6809, and [K/Fe]-[Na/Fe] correlations are found in NGC104 and NGC6752. No evidence of [K/Fe]-[Mg/Fe] anti-correlation are found. The results of our analysis are consistent with a scenario in which the process leading to the multi-populations in globular clusters implies also enrichment in the K abundance, the amplitude of the associated [K/Fe] enhancement becoming measurable only in stars showing the most extreme effects of O and Mg depletion. Stars enhanced in [K/Fe] have been found so far only in clusters harbouring some Mg-poor stars, while the other globulars, without a Mg-poor sub-population, show small or null [K/Fe] spreads.Comment: 9 pages, 7 figures, 3 tables, accepted for publication in A&

RBF neural net based classifier for the AIRIX accelerator fault diagnosis

The AIRIX facility is a high current linear accelerator (2-3.5kA) used for flash-radiography at the CEA of Moronvilliers France. The general background of this study is the diagnosis and the predictive maintenance of AIRIX. We will present a tool for fault diagnosis and monitoring based on pattern recognition using artificial neural network. Parameters extracted from the signals recorded on each shot are used to define a vector to be classified. The principal component analysis permits us to select the most pertinent information and reduce the redundancy. A three layer Radial Basis Function (RBF) neural network is used to classify the states of the accelerator. We initialize the network by applying an unsupervised fuzzy technique to the training base. This allows us to determine the number of clusters and real classes, which define the number of cells on the hidden and output layers of the network. The weights between the hidden and the output layers, realising the non-convex union of the clusters, are determined by a least square method. Membership and ambiguity rejection enable the network to learn unknown failures, and to monitor accelerator operations to predict future failures. We will present the first results obtained on the injector.Comment: 3 pages, 4 figures, LINAC'2000 conferenc

An Economic Risk Analysis of No-Till Rice Management from the Landlord’s Perspective

Rice production generally involves intensive cultivation. The profitability of no-till rice has been investigated but solely from the producer’s perspective. Most farmed cropland is owned by someone else. This study evaluates the risk efficiency of no-till rice from the landlord’s perspective using stochastic efficiency with respect to a function (SERF).Crop Production/Industries,