The beef market in the European Union.

This report analyses the market for beef in the European Union. The information in this report primarily concerns the EU-15 countries. More than half of the beef consumption in the EU is sold as fresh beef products at the retail level, the rest being used in processed products or sold through catering outlets. This report will primarily focus on the market for beef sold fresh at the retail level. The report analyses consumption patterns and looks at various explanations of changes in beef consumption. A complementary analysis of the structure of the beef supply chain will comprise all links from farmers through cattle markets, slaughtering companies and wholesalers to retailers. Such an analysis of the basic structure of the distribution chain is helpful with a view to understanding the market’s use or non-use of marketing parameters like branding, advertising, product quality, and new product development. Section 2 contains an overview of the European beef market including trends in consumption, production and foreign trade as well as a description of some legal and political issues as well as of some factors concerning health and food safety, which are of importance to the beef market participants. In section 3 the beef consumption patterns in the EU are analysed and various factors influencing consumption patterns are considered, including demographic and economic factors as well as the influence of consumer preferences. Section 4 contains an analysis of the beef sector in terms of structure, competition and marketing strategies. Section 5 concludes the report with a summary of key findings. A number of interviews with slaughtering companies, trade organisations and retail chains in the United Kingdom, Greece and Denmark have been conducted to support the compilation of information for the report. The interviews have contributed to a better understanding of the market and they have provided validation for some of the information gathered in the report. The areas covered concern market trends, relations between actors in the distribution chain, product quality, new product development, branding, advertising and promotion. Where interviews contained valuable insights or comments which were difficult to summarise in a meaningful way, the answers have been reproduced in smaller print in nearly full length. Some of the data are incorporated in the text. The interview guidelines are attached in the Appendix. Five interviews were conducted in Denmark, three in the United Kingdom and four in Greece. The companies are classified according to size and market. Codes are used in the report to identify respondents. A legend of codes can be found in the Appendix.Food; Meat products; European Union

X-ray computed tomography data structure tensor orientation mapping for finite element models - STXAE

Accurate modelling of fibre-reinforced composites requires anisotropic material models. Structure tensor analysis of X-ray 3D images has been shown to provide fast and robust estimation of local structural orientations in fibre-reinforced composites. We present two mapping algorithms which can be used to map estimated local orientations onto finite element models for more accurate material modelling. The two functions allow for element-wise and integration point-wise mapping, respectively, and have been implemented using Python in a Jupyter notebook. Together with the previously published structure tensor code, these two functions demonstrate the concept of Structure Tensor X-ray computed tomography Aided Engineering (STXAE) (Phonetics: [stekseɪi:])

Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions

X-ray computed tomography scans can provide detailed information about the state of the material after manufacture and in service. X-ray computed tomography aided engineering (XAE) was recently introduced as an automated process to transfer 3D image data to finite element models. The implementation of a structure tensor code for material orientation analysis in combination with a newly developed integration point-wise fibre orientation mapping allows an easy applicable, computationally cheap, fast, and accurate model set-up. The robustness of the proposed approach is demonstrated on a non-crimp fabric glass fibre reinforced composite for a low resolution case with a voxel size of 64 μm corresponding to more than three times the fibre diameter. Even though 99.8% of the original image data is removed, the simulated elastic modulus of the considered non-crimp fabric composite is only underestimated by 4.7% compared to the simulation result based on the original high resolution scan

A methodology for developing local smart diagnostic models using expert knowledge

© 2015 IEEE. This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the component-based modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular component-based modelling in manufacturing and other domains

SelSus: Towards a reference architecture for diagnostics and predictive maintenance using smart manufacturing devices

© 2015 IEEE. We propose a reference architecture, SelSus (SELf-SUStaining Manufacturing Systems) that aims to enable the provisioning of diagnostic and prognostic capabilities in manufacturing systems that utilize the notions of 'smart' automation devices

Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions

X-ray computed tomography scans can provide detailed information about the state of the material after manufacture and in service. X-ray computed tomography aided engineering (XAE) was recently introduced as an automated process to transfer 3D image data to finite element models. The implementation of a structure tensor code for material orientation analysis in combination with a newly developed integration point-wise fibre orientation mapping allows an easy applicable, computationally cheap, fast, and accurate model set-up. The robustness of the proposed approach is demonstrated on a non-crimp fabric glass fibre reinforced composite for a low resolution case with a voxel size of 64 μm corresponding to more than three times the fibre diameter. Even though 99.8% of the original image data is removed, the simulated elastic modulus of the considered non-crimp fabric composite is only underestimated by 4.7% compared to the simulation result based on the original high resolution scan

Parameter learning algorithms for continuous model improvement using operational data

In this paper, we consider the application of object-oriented Bayesian networks to failure diagnostics in manufacturing systems and continuous model improvement based on operational data. The analysis is based on an object-oriented Bayesian network developed for failure diagnostics of a one-dimensional pick-and-place industrial robot developed by IEF-Werner GmbH.We consider four learning algorithms (batch Expectation-Maximization (EM), incremental EM, Online EM and fractional updating) for parameter updating in the object-oriented Bayesian network using a real operational dataset. Also, we evaluate the performance of the considered algorithms on a dataset generated from the model to determine which algorithm is best suited for recovering the underlying generating distribution. The object-oriented Bayesian network has been integrated into both the control software of the robot as well as into a software architecture that supports diagnostic and prognostic capabilities of devices in manufacturing systems. We evaluate the time performance of the architecture to determine the feasibility of online learning from operational data using each of the four algorithms. © Springer International Publishing AG 2017