185,668 research outputs found
Buckley, J C, NX15108
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/374521Surname: BUCKLEY
Given Name(s) or Initials: J C
Military Service Number or Last Known Location: NX15108
Missing, Wounded and Prisoner of War Enquiry Card Index Number: 6589185896
Item: [2016.0049.06829] "Buckley, J C, NX15108
Slow Fashion and Sustainability: The Luxury Impact
No full textThe fashion industry is contributing to today's sustainability challenge in a number of ways. Despite all the advantages of modernization, the pace of life is getting frantic and societal behaviour is in conflict with natural resources. Thus, an urgent need arises to ensure quality in production and improve social and environmental conditions. In this vein, slow fashion emerges as a revolutionary process, which is sensitive to the impact that production and distribution have on society and ecosystems. This chapter contributes an original discussion by exploring how luxury fashion could be valuable for long-term sustainability. While luxury fashion is growing fast, it is interesting to ask to what extent luxury fashion could have a positive impact on sustainability due to quality, heritage and artisan skills. This chapter looks deeply into (i) how luxury fashion could enhance sustainability through sustainable sourcing and local manufacturing, and (ii) how the slow fashion concept could be further endorsed through luxury
Optimisation in ‘Self-modelling’ Complex Adaptive Systems
No full textWhen a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems
Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks
No full textThe natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks
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Adjoint analysis of Buckley-Leverett and two-phase flow equations
No full textThis paper analyzes the adjoint equations and boundary conditions for porous media flow models, specifically the Buckley-Leverett equation, and the compressible two-phase flow equations in mass conservation form. An adjoint analysis of a general scalar hyperbolic conservation law whose primal solutions include a shock jump is initially presented, and the results are later specialized to the Buckley-Leverett equation. The non-convexity of the Buckley-Leverett flux function results in adjoint characteristics that are parallel to the shock front upstream of the shock and emerge from the shock front downstream of the shock. Thus, in contrast to the behavior of Burgers’ equation where the adjoint is continuous at a shock, the Buckley-Leverett adjoint, in general, contains a discontinuous jump across the shock. Discrete adjoint solutions from space-time discontinuous Galerkin finite element approximations of the Buckley-Leverett equation are shown to be consistent with the derived closed-form analytical solutions. Furthermore, a general result relating the adjoint equations for different (though equivalent) primal equations is used to relate the two-phase flow adjoints to the Buckley-Leverett adjoint. Adjoint solutions from space-time discontinuous Galerkin finite element approximations of the two-phase flow equations are observed to obey this relationship. Keywords: Adjoint solutions; Buckley-Leverett; Two-phase flow; Conservation law; Continuous analysis; Shockwave
Handbook of Research on Global Fashion Management and Merchandising
No full textThe Handbook of Research on Global Fashion Management and Merchandising explores the various facets of effective management procedures within the fashion industry. Featuring research on entrepreneurship, operations management, marketing, business modeling, and fashion technology, this publication is an extensive reference source for practitioners, academics, researchers, and students interested in the dynamics of the fashion industry
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