Software Architecture Reconstruction: A Process-Oriented Taxonomy

To maintain and understand large applications, it is important to know their architecture. The first problem is that unlike classes and packages, architecture is not explicitly represented in the code. The second problem is that successful applications evolve over time, so their architecture inevitably drifts. Reconstructing the architecture and checking whether it is still valid is therefore an important aid. While there is a plethora of approaches and techniques supporting architecture reconstruction, there is no comprehensive software architecture reconstruction state of the art and it is often difficult to compare the approaches. This paper presents a state of the art in software architecture reconstruction approaches.     

Distributed control of production systems

This editorial introduces the special issue of the Elsevier journal, Engineering Application of Artificial Intelligence, on Distributed control of production systems. The current technology in communication and embedded systems allows products and production resources to play a more active role in the production process. This new active capacity will generate major changes in organizations and information systems (e.g., Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES)). New approaches are now required for modelling, testing and assessing the features made possible by the decisional and informational capabilities of these new active entities. One among the many possibilities is to use agents and holons, since agent and holon-based approaches assume interaction between intelligent entities to facilitate the emergence of a global behavior. This special issue thus focuses on the possible applications of distributed approaches for the design, evaluation and implementation of new control architectures for production systems. Both fundamental and applied research papers are presented.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

GM food and neophobia: connecting with the gatekeepers of consumer choice

Resistance to importation of genetically modified (GM) foods in rich countries has deterred governments in many food‐exporting countries from approving the planting of GM food crops for fear of damaging export markets for conventional food. Apart from governments that have actively imposed barriers to entry, another level of resistance can arise from food distribution channel members deciding not to import foods which they believe consumers in their markets will not want. GM foods fall in this category in some markets, particularly in Europe. In China and India, the two most populous consumer markets, pragmatic considerations appear likely to overcome neophobia regarding this technology, provided that benefits are adequately communicated to consumers. Choice‐modelling experiments show that the same may well be true in Europe. Copyright © 2008 Society of Chemical Industry     

Detection of Fibre Waviness Using Ultrasonic Array Scattering Data

Composite materials owe their success to the ability to favour mechanical properties in specific directions whilst minimising the weight of components. Although the composite manufacturing process has been progressively improved, subtle defects such as fibre waviness are still commonplace. Any localised departure of a ply from the desired lay-up direction is known to adversely affect strength. Therefore, manufacturers and end users are interested in detecting defects such as fibre waviness at various stages during prototyping and as part of the manufacturing process. In this paper, an ultrasonic array is used to both image the composite and extract information that characterises the scattering of the interior structure. The scattering information is encoded in the scattering matrix: defined as the far field amplitude of scattered signals from a defect as a function of the incident and scattering angles. A method for extracting the scattering matrix from experimental array data over a spatially localised region is presented. Ultimately this could lead to the ability to map the distribution of scattering behaviour within the composite. The method is demonstrated on composite samples containing various levels of waviness. It is also shown that use of the differences in the scattering matrices can offer the possibility to statistically differentiate wavinesses of different nature and severity.     

Purification of Whole Brown Flaxseed Meal from Coloring Pigments by Treatment in Hydrogen Peroxide Solutions: Impact on Meal Color

The main objective of the present project was the purification of brown flaxseed meal from coloring pigments in order to improve its potential use as ingredient in food formulations. Thus, this work was aimed to study the bleaching of non-defatted brown flaxseed meal in dilute hydrogen peroxide solutions. Bleaching was evaluated according to the Hunter L, (a), and (b) color scale as function of the hydrogen peroxide concentration (1%, 2%, and 3% v/v), the meal concentration (2.5%, 5%, and 10% w/w), the bleaching time (10, 20, and 30?min), and the pH (3, 7, and 9). Effect of all the independent variables was significant. The L value was the most significantly affected parameter and increased as the concentration of hydrogen peroxide increased, but decreased as the concentration of flaxseed meal increased. The L value was observed to be higher at pH?9 than at pH?3 and 7. The b value, which represents the yellowness, was significantly increased from 18.70?±?0.01 (control sample) to 35.45?±?0.04 after the meal was treated with a 3% hydrogen peroxide solution at pH?9. Total phenolics content significantly decreased when the meal was treated with 3% (v/v) hydrogen peroxide solution. This concentration yielded the higher L color value (highly bleached meal).