5,157 research outputs found

    Roles of Business Schools for New Generations of Innovative Business Leaders: A collaborative stakeholders as agents of change perspective

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    In the post economic crisis, the ability of a society to (re)create sustainable social, organisational and business structures is paramount. This need is clearly seen in our global economy where competitive economic realities result in a steady stream of economic activities being outsourced to offshore manufacturing facilities and service centres. The ability to replace this flow with new offerings is largely dependent on a society’s innovative capacity. This research project seeks to understand the roles of business schools in building innovative capacity within young graduates. Interviews and focus groups will be held with UK universities, leading graduate recruiters, entrepreneurs and students to understand the challenge of developing creative capacity from the perspectives of these key stakeholders. It is anticipated that a richer understanding of the context, ‘real world’ requirements and innovative pedagogical approaches will illuminate opportunities narrowing the skill gap as well as identifying the possibility of developing collaborative relationships between these stakeholders. The findings of this study will be pertinent to universities, national and international policy makers, educational institutions and ultimately future generations of students

    Predicting species' tolerance to salinity and alkalinity using distribution data and geochemical modelling: a case study using Australian grasses

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    BACKGROUND AND AIMS: Salt tolerance has evolved many times independently in different plant groups. One possible explanation for this pattern is that it builds upon a general suite of stress-tolerance traits. If this is the case, then we might expect a correlation between salt tolerance and other tolerances to different environmental stresses. This association has been hypothesized for salt and alkalinity tolerance. However, a major limitation in investigating large-scale patterns of these tolerances is that lists of known tolerant species are incomplete. This study explores whether species' salt and alkalinity tolerance can be predicted using geochemical modelling for Australian grasses. The correlation between taxa found in conditions of high predicted salinity and alkalinity is then assessed. METHODS: Extensive occurrence data for Australian grasses is used together with geochemical modelling to predict values of pH and electrical conductivity to which species are exposed in their natural distributions. Using parametric and phylogeny-corrected tests, the geochemical predictions are evaluated using a list of known halophytes as a control, and it is determined whether taxa that occur in conditions of high predicted salinity are also found in conditions of high predicted alkalinity. KEY RESULTS: It is shown that genera containing known halophytes have higher predicted salinity conditions than those not containing known halophytes. Additionally, taxa occurring in high predicted salinity tend to also occur in high predicted alkalinity. CONCLUSIONS: Geochemical modelling using species' occurrence data is a potentially useful approach to predict species' relative natural tolerance to challenging environmental conditions. The findings also demonstrate a correlation between salinity tolerance and alkalinity tolerance. Further investigations can consider the phylogenetic distribution of specific traits involved in these ecophysiological strategies, ideally by incorporating more complete, finer-scale geochemical information, as well as laboratory experiments.This work was supported by the Australian Research Council

    Discretely exact derivatives for hyperbolic PDE-constrained optimization problems discretized by the discontinuous Galerkin method

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    This paper discusses the computation of derivatives for optimization problems governed by linear hyperbolic systems of partial differential equations (PDEs) that are discretized by the discontinuous Galerkin (dG) method. An efficient and accurate computation of these derivatives is important, for instance, in inverse problems and optimal control problems. This computation is usually based on an adjoint PDE system, and the question addressed in this paper is how the discretization of this adjoint system should relate to the dG discretization of the hyperbolic state equation. Adjoint-based derivatives can either be computed before or after discretization; these two options are often referred to as the optimize-then-discretize and discretize-then-optimize approaches. We discuss the relation between these two options for dG discretizations in space and Runge-Kutta time integration. Discretely exact discretizations for several hyperbolic optimization problems are derived, including the advection equation, Maxwell's equations and the coupled elastic-acoustic wave equation. We find that the discrete adjoint equation inherits a natural dG discretization from the discretization of the state equation and that the expressions for the discretely exact gradient often have to take into account contributions from element faces. For the coupled elastic-acoustic wave equation, the correctness and accuracy of our derivative expressions are illustrated by comparisons with finite difference gradients. The results show that a straightforward discretization of the continuous gradient differs from the discretely exact gradient, and thus is not consistent with the discretized objective. This inconsistency may cause difficulties in the convergence of gradient based algorithms for solving optimization problems

    Partitioning Complex Networks via Size-constrained Clustering

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    The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferring the solution to the next finer graph and applying a local search algorithm to improve the current solution. In this paper, we describe a novel approach to partition graphs effectively especially if the networks have a highly irregular structure. More precisely, our algorithm provides graph coarsening by iteratively contracting size-constrained clusterings that are computed using a label propagation algorithm. The same algorithm that provides the size-constrained clusterings can also be used during uncoarsening as a fast and simple local search algorithm. Depending on the algorithm's configuration, we are able to compute partitions of very high quality outperforming all competitors, or partitions that are comparable to the best competitor in terms of quality, hMetis, while being nearly an order of magnitude faster on average. The fastest configuration partitions the largest graph available to us with 3.3 billion edges using a single machine in about ten minutes while cutting less than half of the edges than the fastest competitor, kMetis

    An improved time of flight gamma-ray telescope to monitor diffuse gamma-ray in the energy range 5 MeV - 50 MeV

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    A time of flight measuring device is the basic triggering system of most of medium and high energy gamma-ray telescopes. A simple gamma-ray telescope has been built in order to check in flight conditions the functioning of an advanced time of flight system. The technical ratings of the system are described. This telescope has been flown twice with stratospheric balloons, its axis being oriented at various Zenital directions. Flight results are presented for diffuse gamma-rays, atmospheric secondaries, and various causes of noise in the 5 MeV-50 MeV energy range

    Structure-specified H∞ loop shaping control for balancing of bicycle robots: A particle swarm optimization approach

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    In this paper, the particle swarm optimization (PSO) algorithm was used to design the structure-specified H∞ loop shaping controllers for balancing of bicycle robots. The structure-specified H∞ loop shaping controller design normally leads to a complex optimization problem. PSO is an efficient meta-heuristic search which is used to solve multi-objectives and non-convex optimizations. A model-based systematic procedure for designing the particle swarm optimization-based structure-specified H∞ loop shaping controllers was proposed in this research. The structure of the obtained controllers are therefore simpler. The simulation and experimental results showed that the robustness and efficiency of the proposed controllers was gained when compared with the proportional plus derivative (PD) as well as conventional H∞ loop shaping controller. The simulation results also showed a better efficiency of the developed control algorithm compared to the Genetic Algorithm based one
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