The relationship between the initial grain size and the critical Zener-Hollomon parameter value (D 0 -Z c ) defines the conditions for which a material will dynamically recrystallize with a single or with multiple peaks. The relationship between the stable dynamically recrystallized grain and the Zener-Hollomon parameter (D rex -Z) predicts the conditions for grain refinement or coarsening during dynamic recrystallization. The Relative-Grain-Size model (D 0 -Z c and D rex -Z) adequately predicts the type of hot flow behavior before reaching a stable dynamically recrystallized grain size. However, a model to reliably predict the stress-strain curve is still needed. Several models exist which have been shown to predict the transition from single to multiple peak stresses. Nevertheless few of them report real material parameters and in any case the computational time makes them unviable for any industrial simulation process. The present authors have devised a DRX algorithm to measure the stress due to the diminishing initial grain volume and to measure the correction stress due to recrystallizing grains. One stress contribution is produced as a result of the surrounding or percolating new grains and another stress is due to the response of deforming the initial grain volume. The present authors propose a relatively simple model that in conjunction with existing theories for dynamic recovery can quantitatively predict the transition from single to multiple peak stress behavior during dynamic recrystallization. The predicted stress-strain curves have been correlated to experimental results after compression testing (650ºC-950ºC) commercially 99.9% pure copper.

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The integration of multi-centre medical image data to create knowledge repositories for research and training activities has been an aim targeted since long ago. This paper presents an environment to share, to process and to organise medical imaging data according to a structured framework in which the image reports play a key role. This environment has been validated on a clinical environment, facing problems such as firewalls and security restrictions, in the frame of the CVIMO (Valencian Cyberinfrastructure of Medical Imaging in Oncology) project. The environment uses a middleware called TRENCADIS (Towards a Grid Environment for Processing and Sharing DICOM Objects) that provides users with the management of multiple administrative domains, data encryption and decryption on the fly and semantic indexation of images. Data is structured into four levels: Global data available, virtual federated storages of studies shared across a vertical domain, subsets for projects or experiments on the virtual storages and individual searches on these subsets. This structure of levels gives the needed flexibility for organising authorisation, and hides data that are not relevant for a given experiment. The main components and interactions are shown in the document, outlining the workflows and explaining the different approaches considered, including the protocols used and the difficulties met.

As the size and complexity of Cloud systems increases, the manual management of these solutions becomes a challenging issue as more personnel, resources and expertise are needed. Service Level Agreement (SLA)-aware autonomic cloud solutions enable managing large scale infrastructure management meanwhile supporting multiple dynamic requirement from users. This paper contributes to these topics by the introduction of Cloudcompaas, a SLA-aware PaaS Cloud platform that manages the complete resource lifecycle. This platform features an extension of the SLA specification WSAgreement, tailored to the specific needs of Cloud Computing. In particular, Cloudcompaas enables Cloud providers with a generic SLA model to deal with higher-level metrics, closer to end-user perception, and with flexible composition of the requirements of multiple actors in the computational scene. Moreover, Cloudcompaas provides a framework for general Cloud computing applications that could be dynamically adapted to correct the QoS violations by using the elasticity features of Cloud infrastructures. The effectiveness of this solution is demonstrated in this paper through a simulation that considers several realistic workload profiles, where Cloudcompaas achieve minimum cost and maximum efficiency, under highly heterogeneous utilization patterns.