European mineral statistics 2009-13 : a product of the World Mineral Statistics database

This volume is the latest edition of a series that began in 2002 following the replacement of ‘World Mineral Statistics’ with ‘World Mineral Production’. It contains mineral production, import and export data for more than 70 mineral commodities, for 36 European countries including all EU Member States and EU Candidate Countries, plus Norway and Switzerland. These data are presented in two sections: by individual country and by commodity; the latter is illustrated by graphics. It remains the only freely available and up-to-date publication dedicated to statistical information about minerals and metals in Europe

European Mineral Statistics 2010-14: a product of the World Mineral statistics database

European Mineral Statistics provides statistical information about minerals and metals in Europe. It provides the essential background intelligence for any European minerals-related activities. Production, export and import tables are presented for all EU members and EU candidate countries, plus Norway and Switzerland, in two sections: •by individual country •by commodity, with bullets on salient features and graphics More than 70 different mineral commodities are included from Aluminium to Zirconium, plus statistics relating to primary aggregates and cement. The book also includes commentary discussing the different categories of minerals – construction minerals, industrial minerals, metals and energy minerals – in the European context and contains general information on the compilation of data

Structured machine learning tools for modelling characteristics of guided waves

The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM). The use of high-frequency waves such as these offers an advantage over low-frequency methods from their ability to detect damage on a smaller scale. However, in order to assess damage in a structure, and implement any NDE or SHM tool, knowledge of the behaviour of a guided wave throughout the material/structure is important (especially when designing sensor placement for SHM systems). Determining this behaviour is extremely difficult in complex materials, such as fibre–matrix composites, where unique phenomena such as continuous mode conversion takes place. This paper introduces a novel method for modelling the feature-space of guided waves in a composite material. This technique is based on a data-driven model, where prior physical knowledge can be used to create structured machine learning tools; where constraints are applied to provide said structure. The method shown makes use of Gaussian processes, a full Bayesian analysis tool, and in this paper it is shown how physical knowledge of the guided waves can be utilised in modelling using an ML tool. This paper shows that through careful consideration when applying machine learning techniques, more robust models can be generated which offer advantages such as extrapolation ability and physical interpretation

A Bayesian method for material identification of composite plates via dispersion curves

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber)—which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser Doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate. The distribution of the material properties at each angle is discussed, including the inferred confidence in the predicted parameters. The percentage errors of the estimated values for the parameters were 10–15 points larger when using the most likely estimates, as opposed to calculating from the posterior distributions, highlighting the advantages of using a probabilistic approach

Spectrally tunable chiral Bragg reflectors for on-demand beam generation

We demonstrate the generation of spectrally tunable phase-dependent wavefronts, using the 2D Airy as the primary test case, via a polymer-stabilized cholesteric liquid crystal (PSCLC) element. Specifically, we use a novel spatial light modulator (SLM) based projection system to photo-align the initial helix angle landscape of the PSCLC so that it imparts the appropriate cubic phase profile to the reflected beam. This element is spectrally selective, with a reflection bandwidth of &asymp; 100 nm, and electrically tunable from &lambda; = 530 nm to 760 nm. Under both green and red laser illumination, the element is shown to conditionally form an Airy beam depending on the position of the electrically tailored reflection band. We briefly demonstrate the generality of this approach by producing PSCLC elements which form a computer-generated hologram and a higher-order Mathieu beam.</p

Full-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods

Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what can be learned from measured vibration data. However, significant challenges remain in the tasks of identification, prediction and evaluation of full-scale structures. A significant aid in the roadmap to the application of cutting-edge methods to the demands of in-service engineering structures, is the development of comprehensive benchmark datasets. With the aim of developing a useful and worthwhile benchmark dataset for structural dynamics, an extensive testing campaign is presented here. This recent campaign was performed on a decommissioned BAE system Hawk T1A aircraft at the Laboratory for Verification and Validation (LVV) in Sheffield. The aim of this paper is to present the dataset, providing details on the structure, experimental design, and data acquired. The collected data is made freely and openly available with the intention that it serve as a benchmark dataset for challenges in full-scale structural dynamics. Here, the details pertaining to two test phases (frequency and time domain) are presented. So as to ensure that the presented dataset is able to function as a benchmark, some baseline-level results are additionally presented for the tasks of identification and prediction, using standard approaches. It is envisaged that advanced methodologies will demonstrate superiority by favourable comparison with the results presented here. Finally, some dataset-specific challenges are described, with a view to form a hierarchy of tasks and frame discussion over their relative difficulty

A Bayesian method for material identification of composite plates via dispersion curves

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully-defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber) -- which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate

The role of manufacturing and market managers in strategy development:lessons from three companies

According to researchers and managers, there is a lack of agreement between marketing and manufacturing managers on critical strategic issues. However, most of the literature on the subject is anecdotal and little formal empirical research has been done. Three companies are investigated to study the extent of agreement/disagreement between manufacturing and marketing managers on strategy content and process. A novel method permits the study of agreement between the two different functional managers on the process of developing strategy. The findings consistently show that manufacturing managers operate under a wider range of strategic priorities than marketing managers, and that manufacturing managers participate less than marketing managers in the strategy development process. Further, both marketing and manufacturing managers show higher involvement in the strategy development process in the latter stages of the Hayes and Wheelwright four-stage model of manufacturing’s strategic role

‘The Brick’ is not a brick: a comprehensive study of the structure and dynamics of the central molecular zone cloud G0.253+0.016

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society © 2019 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.In this paper we provide a comprehensive description of the internal dynamics of G0.253+0.016 (a.k.a. ‘the Brick’); one of the most massive and dense molecular clouds in the Galaxy to lack signatures of widespread star formation. As a potential host to a future generation of high-mass stars, understanding largely quiescent molecular clouds like G0.253+0.016 is of critical importance. In this paper, we reanalyse Atacama Large Millimeter Array cycle 0 HNCO J = 4(0, 4) − 3(0, 3) data at 3 mm, using two new pieces of software that we make available to the community. First, SCOUSEPY, a Python implementation of the spectral line fitting algorithm SCOUSE. Secondly, ACORNS (Agglomerative Clustering for ORganising Nested Structures), a hierarchical n-dimensional clustering algorithm designed for use with discrete spectroscopic data. Together, these tools provide an unbiased measurement of the line-of-sight velocity dispersion in this cloud, σvlos,1D=4.4±2.1 km s−1, which is somewhat larger than predicted by velocity dispersion-size relations for the central molecular zone (CMZ). The dispersion of centroid velocities in the plane of the sky are comparable, yielding σvlos,1D/σvpos,1D∼1.2±0.3⁠. This isotropy may indicate that the line-of-sight extent of the cloud is approximately equivalent to that in the plane of the sky. Combining our kinematic decomposition with radiative transfer modelling, we conclude that G0.253+0.016 is not a single, coherent, and centrally condensed molecular cloud; ‘the Brick’ is not a brick. Instead, G0.253+0.016 is a dynamically complex and hierarchically structured molecular cloud whose morphology is consistent with the influence of the orbital dynamics and shear in the CMZ

The Value of Information for Populations in Varying Environments

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. Here, we present a model of population dynamics where this problem is amenable to a mathematical analysis. In the limit where any information about future environmental variations is common to the members of the population, our model is equivalent to known models of financial investment. In this case, the population can be interpreted as a portfolio of financial assets and previous analyses have shown that a key quantity of Shannon's communication theory, the mutual information, sets a fundamental limit on the value of information. We show that this bound can be violated when accounting for features that are irrelevant in finance but inherent to biological systems, such as the stochasticity present at the individual level. This leads us to generalize the measures of uncertainty and information usually encountered in information theory