Building collaborative environments for advanced computing

Synchronous collaborative systems allow geographically distributed users to form a virtual work environment enabling cooperation between peers and enriching the human interaction. The technology facilitating this interaction has been studied for several years and various solutions can be found at present. In this paper, we discuss our experiences with one such widely adopted technology, namely the Access Grid [1]. We describe our experiences with using this technology, identify key problem areas and propose our solution to tackle these issues appropriately. Moreover, we propose the integration of Access Grid with an Application Sharing tool, developed by the authors. Our approach allows these integrated tools to utilise the enhanced features provided by our underlying dynamic transport layer.

Multi-agent building control in shared environment

Multi-agent systems have been adopted to build intelligent environment in recent years. It was claimed that energy efficiency and occupants' comfort were the most important factors for evaluating the performance of modem work environment, and multi-agent systems presented a viable solution to handling the complexity of dynamic building environment. While previous research has made significant advance in some aspects, the proposed systems or models were often not applicable in a "shared environment". This paper introduces an ongoing project on multi-agent for building control, which aims to achieve both energy efficiency and occupants' comfort in a shared environment.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Exact error estimates and optimal randomized algorithms for integration

Exact error estimates for evaluating multi-dimensional integrals are considered. An estimate is called exact if the rates of convergence for the low- and upper-bound estimate coincide. The algorithm with such an exact rate is called optimal. Such an algorithm has an unimprovable rate of convergence. The problem of existing exact estimates and optimal algorithms is discussed for some functional spaces that define the regularity of the integrand. Important for practical computations data classes are considered: classes of functions with bounded derivatives and Holder type conditions. The aim of the paper is to analyze the performance of two optimal classes of algorithms: deterministic and randomized for computing multidimensional integrals. It is also shown how the smoothness of the integrand can be exploited to construct better randomized algorithms.

Swarms of computational resources and swarms of tasks for building reliable systems

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve autonomy for distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Building communities via a learning landscape

The Learning Landscape project described here is known as RedGloo and has several objectives; among others it aims to help students to make friends, contacts and join communities based on interests and competencies. RedGloo provides a space where students can support each other with personal, academic and career development, sharing insights gained from extracurricular activities as well as their degree programmes. It has shown tendencies of becoming a learning community with several communities of practice.

Export of dissolved organic carbon from an upland peatland during storm events: implications for flux estimates

Most of the dissolved organic carbon (DOC) exported from catchments is transported during storm events. Accurate assessments of DOC fluxes are essential to understand long-term trends in the transport of DOC from terrestrial to aquatic systems, and also the loss of carbon from peatlands to determine changes in the source/sink status of peatland carbon stores. However, many long-term monitoring programmes collect water samples at a frequency (e.g. weekly/monthly) less than the time period of a typical storm event (typically <1–2 days). As widespread observations in catchments dominated by organo-mineral soils have shown that both concentration and flux of DOC increases during storm events, lower frequency monitoring could result in substantial underestimation of DOC flux as the most dynamic periods of transport are missed. However, our intensive monitoring study in a UK upland peatland catchment showed a contrasting response to these previous studies. Our results showed that (i) DOC concentrations decreased during autumn storm events and showed a poor relationship with flow during other seasons; and that (ii) this decrease in concentrations during autumn storms caused DOC flux estimates based on weekly monitoring data to be over-estimated, rather than under-estimated, because of over rather than under estimation of the flow-weighted mean concentration used in flux calculations. However, as DOC flux is ultimately controlled by discharge volume, and therefore rainfall, and the magnitude of change in discharge was greater than the magnitude of decline in concentrations, DOC flux increased during individual storm events. The implications for long-term DOC trends are therefore contradictory, as increased rainfall could increase flux but cause an overall decrease in DOC concentrations from peatland streams. Care needs to be taken when interpreting long-term trends in DOC flux rather than concentration; as flux is calculated from discharge estimates, and discharge is controlled by rainfall, DOC flux and rainfall/discharge will always be well correlated.

Error estimates for a fully discrete spectral scheme for a class of nonlinear, nonlocal dispersive wave equations

We analyze a fully discrete spectral method for the numerical solution of the initial- and periodic boundary-value problem for two nonlinear, nonlocal, dispersive wave equations, the Benjamin–Ono and the Intermediate Long Wave equations. The equations are discretized in space by the standard Fourier–Galerkin spectral method and in time by the explicit leap-frog scheme. For the resulting fully discrete, conditionally stable scheme we prove an L2-error bound of spectral accuracy in space and of second-order accuracy in time.