8,092 research outputs found

    Stochastic Lot Sizing for Shareholder Wealth Maximisation under Carbon Footprint Management

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    Fulltext in http://www.jiii.org/index.php?m=content&c=index&a=show&catid=41&id=141There is a growing consensus that human beings must cut greenhouse gas emissions to mitigate global warming and the resultant impacts on the environment. However, production optimisation has rarely taken this issue into consideration, often leading to environmentally unsustainable operation decisions. This paper presents a lot sizing batch optimisation model for a stochastic make-to-order production environment under the carbon emission trading mechanism—currently the most effective market-based carbon emission controlling system, with an aim to maximise the long-term sustainable interests of corporate owners, well-known as the shareholder wealth. To more closely reflect the real-world manufacturing environment, the proposed model adopts general distributions, instead of unrealistic theoretical assumptions, for random variables. We apply the model to investigate the impacts of the carbon emission trading mechanism on shareholder wealth, and test its hedging capability against a series of risk factors. The analytical results provide insights into production optimisation with carbon footprint management.International Conference on Industrial Engineering and Applications (ICIEA 2014), Sydney, Australia, 29-30 May 2014. In Journal of Industrial and Intelligent Information, 2015, v. 3 n. 1, p. 1-

    Hole dispersions in the G- and C-type orbital ordering backgrounds: Doped manganese oxides

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    In the framework of the linear spin-wave theory and orbital-charge separation, we calculate quasiparticle (QP) dispersions for two different antiferromagnetic orbital structures in the fully saturated spin phase of manganese oxides. Although with the same orbital wave excitations, the QP bands of C- and G-type orbital structures exhibit completely different shapes. The pseudogap observed in the density of states and spectral functions around ω=0 is related with the large antiferromagnetic orbital fluctuation. The minimal band energy for G-type is lower than that for C-type orbital order, while these band curves almost coincide in some momentum points. Larger energy splitting occurs between the two branches of k z=0 and k z=π when increasing the superexchange coupling J, suggesting that the orbital scattering plays an essential role in the QP dispersions. ©2000 The American Physical Society.published_or_final_versio

    Associative classifier for uncertain data

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    Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. And uncertainty may render many conventional classifiers inapplicable to uncertain classification tasks. In this paper, based on U-Apriori algorothm and CBA algorithm, we propose an associative classifier for uncertain data, uCBA (uncertain Classification Based on Associative), which can classify both certain and uncertain data. The algorithm redefines the support, confidence, rule pruning and classification strategy of CBA. Experimental results on 21 datasets from UCI Repository demonstrate that the proposed algorithm yields good performance and has satisfactory performance even on highly uncertain data

    Asymmetric hydroformylation of styrene catalyzed by pyranoside diphosphite-rh(Ⅰ) complexes

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    2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Localized-density-matrix, segment-molecular-orbitals and poly(p-phenylenevinylene) aggregates

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    The segment-molecular-orbital representation is developed and incorporated into the recently developed linear-scaling localized-density-matrix method. The entire system is divided into many segments, and the molecular orbitals of all segments form the basis functions of the segment-molecular-orbital representation. Introduction of different cutoff lengths for different segment-molecular-orbitals leads to a drastic reduction of the computational cost. As a result, the modified localized-density-matrix method is employed to investigate the optical responses of large Poly(p-phenylenevinylene) aggregates. In particular, the interchain excitations are studied. The complete neglect of differential overlap in spectroscopy hamiltonian is employed in the calculation. © 1999 American Institute of Physics.published_or_final_versio

    Correlation effects in the electronic structure of the Ni-based superconducting KNi2S2

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    Characteristics and community diversity of a wheat straw-colonizing microbial community

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    A microbial community named WSD-5 was successfully selected from plant litter and soil after longterm directed acclimation at normal temperature. After 15 days of cultivation at 30°C, the degradation rate of wheat straw by WSD-5 was 75.6%. For cellulose, hemicellulose and lignin, the degradation rates were 94.2, 81.9 and 21.3%, respectively. The optimal pH for filter paper, CMCase, avicelase, β- glucosidase and xylanse activities was 6.24, 6.24, 5.91, 5.91 and 6.24, respectively. The optimal reaction temperature for all enzyme activity was 55°C. Filter paper enzyme, cellulase and xylanase were secreted from the degradation of wheat straw by WSD-5. The highest filter paper, cellulose endonuclease (CMCase), cellulose exonuclease (avicelase), β-glucosidase and xylanase activities were 1.30, 4.35, 0.60, 0.43 and 15.16 U/ml, respectively. A library of bacterial and fungal ribosomal gene sequences obtained from the community showed the presence of Ochrobactrum sp, Coprinus cinereus and Pseudallescheria boydii. To our knowledge, this was the first report of a microbial community which consisted of bacteria and fungi and was selected in the presence of wheat straw to produce extracellular filter paper enzyme, cellulase and xylanase.Key words: Microbial community, lignocellulose degradation, filter paper activity, CMCase, avicelase, β- glucosidase, xylanase

    Combined first-principles calculation and neural-network correction approach for heat of formation

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    A promising new approach to improve the results of first-principles quantum mechanical calculations and to calibrate their uncertainties is presented. The approach is highly efficient compared to much more sophisticated first-principles methods of similar accuracy, and more importantly, is expected to be applied to much larger systems.published_or_final_versio

    PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

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    Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd