Combinatorial chemistry and the Grid

Chemistry has always made extensive use of the developing computing technology and available computing power though activities such as modelling, simulation and chemical structure interpretational - activities conveniently summarised as computational chemistry. Developing procedures in chemical synthesis and characterisation, particularly in the arena of parallel and combinatorial methodology, have generated ever increasing demands on both Computational Chemistry and Computer Technology. Significantly, the way in which networked services are being conceived to assist collaborative research pushes the use of data acquisition, remote interaction & control, computation, and visualisation, well beyond the traditional computational chemistry programmes, towards the basic issue of handling chemical information and knowledge. The rate at which new chemical data can now be generated in Combinatorial and Parallel synthesis and screening processes, means that the data can only realistically be handled efficiently by increased automation of the data analysis as well as the experimentation and collection. Without this automation we run the risk of generating information without the ability to understand it

Bayesian challenges in integrated catchment modelling

Bayesian Networks (BNs) are increasingly being used as decision support tools to aid the management of the complex and uncertain domains of natural systems. They are particularly useful for addressing problems of natural resource management by complex data analysis and incorporation of expert knowledge. BNs are useful for clearly articulating both the assumptions and evidence behind the understanding of a problem, and approaches for managing a problem. For example they can effectively articulate the cause effect relationships between human interventions and ecosystem functioning, which is a major difficulty faced by planners and environment managers. The flexible architecture and graphical representation make BNs attractive tools for integrated modelling. The robust statistical basis of BNs provides a mathematically coherent framework for model development, and explicitly represents the uncertainties in model predictions. However, there are also a number of challenges in their use. Examples include i) the need to express conditional probabilities in discrete form for analytical solution, which adds another layer of uncertainty; ii) belief updating in very large Bayesian networks; iii) difficulties associated with knowledge elicitation such as the range of questions to be answered by experts, especially for large networks; iv) the inability to incorporate feedback loops and v) inconsistency associated with incomplete training data. In this paper we discuss some of the key research problems associated with the use of BNs as decision-support tools for environmental management. We provide some real-life examples from a current project (Macro Ecological Model) dealing with the development of a BN-based decision support tool for Integrated Catchment Management to illustrate these challenges. We also discuss the pros and cons of some existing solutions. For example, belief updating in very large BNs cannot be effectively addressed by exact methods (NP hard problem), therefore approximate inference schemes may often be the only computationally feasible alternative. We will also discuss the discretisation problem for continuous variables, solutions to the problem of missing data, and the implementation of a knowledge elicitation framework

Bayesian networks for a multi-objective evaluation of River Basin Management Plans

The European Water Framework Directive (WFD) sets out an integrated perspective to water management in river catchments and river basin districts and is a key driver in the movement towards Integrated River Basin Management. Integrated river basin management must deliver objectives related to the WFD in the wider context of various other stakeholder interests, for example related to flooding, water resources, employment and cost. In managing such complex systems, a specific objective can be achieved through different management actions. Likewise, a specific management action can have implications for multiple objectives. Synergies or conflicts between specific objectives and between specific actions are likely to occur, and need careful consideration in order to increase the efficiency of planned management actions. However, such integrated decision making is a very difficult and highly complex task, which cannot easily be accomplished by either single or groups of planners. Integrated modelling tools to facilitate and enhance communication within a group of decision-makers and inform a more objective and evidence-based multi-criteria decision-making process are required. The scope for the development of such an integrated tool is being tested by the Catchment Science Centre (CSC) at The University of Sheffield. The CSC and the Environment Agency are jointly developing a tool termed the Macro-Ecological Model (MEM). The MEM is developed as a consistent framework for the integration of knowledge and information about environmental, social and economic processes and process-interactions that are affected by management actions and have impacts on multiple management objectives. The MEM enables knowledge from various different resources to be integrated, including empirical data, model results and even expert knowledge using a Bayesian Belief Network (BBN) approach. BBNs have the advantage of representing system understanding in an intuitive, graphical format. Furthermore, the approach provides the ability to explicitly account for uncertainties in model predictions. Therefore, the model framework provides a good tool for visualising system understanding and communicating uncertainties. Applied in a participatory process, it can support robust decision making in river basin management. The conceptual model framework is illustrated with examples from the prototyping study. The prototype model captures the process interactions affecting the management objectives "Ecological Status" (composed of both Biological Quality and Physico-chemical Quality) and "Flood Risk". It is planned to be later extended to incorporate further environmental, and also social and economic objectives

Resource Discovery for Dynamic Clusters in Computational Grids

We describe a de-centralised approach to resource management and discovery, based on a community of interacting software agents. Each agent either represents a user application, a resource, or a MatchMaking service. The proposed approach can support dynamic registration of resources and user tasks, facilitating the establishment of dynamic clusters. Resource capability and task requirements are described using an object based data model, enabling new types of devices or new features in existing devices to be identified. A comparison with the Discovery and LookUp services in Jini and TSpaces is also provided

A Review on Eye-Tracking Metrics for Sleepiness

Sleepiness that can arise from sleep deprivation can increase human errors in task performance and create workplace hazards and accidents. Hence, it is critical to detect sleepiness to minimize hazards and human errors. This paper provides a review of the literature on eye tracking metrics that can be used to detect sleepiness. These metrics include blink duration, blink frequency, saccade latency, saccade peak velocity, saccade accuracy, smooth pursuit velocity gain, fixation rate, pupil size, and latency to pupil constriction