E-health makes use of information and communication methods and the latest e-research tools to support the understanding of body functions. E-scientists in this field take already advantage of one single infrastructure to perform computationally-intensive investigations of the human body that tend to consider each of the constituent parts separately without taking into account the multiple important interactions between them. But these important interactions imply an increasing complexity of applications that embrace multiple physical models (i.e. multi-physics) and consider a larger range of scales (i.e. multi-scale) thus creating a steadily growing demand for interoperable infrastructures that allow for new innovative application types of jointly using different infrastructures for one application. But interoperable infrastructures are still not seamlessly provided and we argue that this is due to the absence of a realistically implementable infrastructure interoperability reference model that is based on lessons learned from e-science usage. Therefore, the goal of this paper is to explore the potential of using multiple infrastructures for one scientific goal with a particular focus on e-health. Since e-scientists gain more interest in using multiple infrastructures there is a clear demand for interoperability between them to enable a use with one e-research tool. The paper highlights work in the context of an e-Health blood flow application while the reference model is applicable to other e-science applications as well.

Large scale supercomputing applications typically run on clusters using vendor message passing libraries, limiting the application to the availability of memory and CPU resources on that single machine. The ability to run inter-cluster parallel code is attractive since it allows the consolidation of multiple large scale resources for computational simulations not possible on a single machine, and it also allows the conglomeration of small subsets of CPU cores for rapid turnaround, for example, in the case of high-availability computing. MPIg is a grid-enabled implementation of the Message Passing Interface (MPI), extending the MPICH implementation of MPI to use Globus Toolkit services such as resource allocation and authentication. To achieve co-availability of resources, HARC, the Highly-Available Resource Co-allocator, is used. Here we examine two applications using MPIg: LAMMPS (Large-scale Atomic/ Molecular Massively Parallel Simulator), is used with a replica exchange molecular dynamics approach to enhance binding affinity calculations in HIV drug research, and HemeLB, which is a lattice-Boltzmann solver designed to address fluid flow in geometries such as the human cerebral vascular system. The cross-site scalability of both these applications is tested and compared to single-machine performance. In HemeLB, communication costs are hidden by effectively overlapping non-blocking communication with computation, essentially scaling linearly across multiple sites, and LAMMPS scales almost as well when run between two significantly geographically separated sites as it does at a single site.

To help unravel the structure of the universe, astronomers have developed systems which observe large clusters of objects at the same time. One such system is the 2-degree field spectrograph at the Anglo Australian Observatory. This system uses 400 fibres which can observe and measure the spectra of up to 400 astronomical objects in parallel. These optical fibres are positioned by a robotic arm. During placement, complex fibre entanglements often occur, reducing the overall efficiency of the system. This work aims to develop a combinatorial genetic algorithm which can help increase the efficiency of these systems by evolving sequences of fibre re-positionings. We present an integer-based representation developed for this GA which has the ability to automatically satisfy hard constraints during sequence seeding, mutation and recombination. We also present some initial results in re-configuring fibre placements.

Microstructured optical fibres are a relatively recent advance in fibre technology which guide light by using arrays of air holes which run the length of the fibre. The internal microstructure of optical fibres can be altered to reshape and transform light for use in medical applications, sensing, long distance and local area network high bandwidth communications. Recent progress in the production of polymer fibres allows designs with complex microstructures consisting of hundreds of holes to be manufactured. In this paper we present a generative (embryogenic) representation which can produce symmetric fibre designs with a variable number of holes. The resulting genetic algorithm has the ability to search designs of varying complexity over time, allowing less or more complex designs to be evolved as required. Various aspects of this representation are discussed in light of the supporting genetic algorithm such as as recombination of designs and the conversion of the variable length binary genotype to the phenotype (optical fibre structure). We include some single objective design results for a high-bandwidth optical fibre along with manufactured designs.

A multiobjective evolutionary optimisation algorithm is applied to a fibre Bragg grating (optical filter) design problem. The design specified a dual wavelength filter with four required spectral characteristics - total bandwidth, peak separation, peak width and minimum transmission. Five parameters which described the apodised grating profile were used to define the search space and the transfer matrix method was used to numerically evaluate the transmission spectrum of candidate solutions. Various constraints on the search space were included in the design algorithm. Two separate selection schemes were tested, a distance based approach as used in the nondominated sorting genetic algorithm (NSGA-II) and a conglomerative clustering approach as used in the strength Pareto evolutionary algorithm (SPEA). Nondominated solutions are found and it is evident that particular objectives can be achieved more easily than others. Preliminary results are discussed and future work is introduced.