An agent based approach to distributed task modeling for co-operative task support

This paper describes the design and implementation of an agent based network for the support of collaborative switching tasks within the control room environment of the National Grid Company plc. This work includes aspects from several research disciplines, including operational analysis, human computer interaction, finite state modelling techniques, intelligent agents and computer supported co-operative work. Aspects of these procedures have been used in the analysis of collaborative tasks to produce distributed local models for all involved users. These models have been used as the basis for the production of local finite state automata. These automata have then been embedded within an agent network together with behavioural information extracted from the task and user analysis phase. The resulting support system is capable of task and communication management within the transmission despatch environment.

Cooperative task support for the control room

The National Grid Company plc. owns and operates the electricity transmission network in England and Wales, the day to day running of the network being carried out by teams of engineers within the national control room. The task of monitoring and operating the transmission network involves the transfer of large amounts of data and a high degree of cooperation between these engineers. The purpose of the research detailed in this paper is to investigate the use of interfacing techniques within the control room scenario, in particular, the development of an agent based architecture for the support of cooperative tasks. The proposed architecture revolves around the use of interface and user supervisor agents. Primarily, these agents are responsible for the flow of information to and from individual users and user groups. The agents are also responsible for tackling the synchronisation and control issues arising during the completion of cooperative tasks. In this paper a novel approach to human computer interaction (HCI) for power systems incorporating an embedded agent infrastructure is presented. The agent architectures used to form the base of the cooperative task support system are discussed, as is the nature of the support system and tasks it is intended to support.

The distributed work of local action: interaction amongst virtually collocated research teams

Existing research on synchronous remote working in CSCW has highlighted the troubles that can arise because actions at one site are (partially) unavailable to remote colleagues. Such ‘local action’ is routinely characterised as a nuisance, a distraction, subordinate and the like. This paper explores interconnections between ‘local action’ and ‘distributed work’ in the case of a research team virtually collocated through ‘MiMeG’. MiMeG is an e-Social Science tool that facilitates ‘distributed data sessions’ in which social scientists are able to remotely collaborate on the real-time analysis of video data. The data are visible and controllable in a shared workspace and participants are additionally connected via audio conferencing. The findings reveal that whilst the (partial) unavailability of local action is at times problematic, it is also used as a resource for coordinating work. The paper considers how local action is interactionally managed in distributed data sessions and concludes by outlining implications of the analysis for the design and study of technologies to support group-to-group collaboration.

Coping with linguistic challenges in UK higher education: the use of strategies by Chinese research students

The present study sets out to examine the strategies used by Chinese learners in a predominantly naturalistic environment and how such learner strategy use relates to their proficiency in the second language. Data were collected from four Chinese research students in the UK using semi-structured interviews. Their proficiency in English was assessed with an oral interview and a listening test. The main findings from this study are that the learners used a wide range of strategies overall, including metacognitive, cognitive, social/affective and compensation strategies. The majority of the commonly reported strategies were metacognitive strategies, suggesting that the learners were self-directed and attempting to manage their own learning in an informal context. They also showed idiosyncrasies in their use of learner strategies. Attempts to explain the learners’ strategy use in relation to their levels of proficiency in English and contextual factors, as well as several other factors, are offered. Implications for target-country institutions in terms of the provision of support to Chinese students are discussed.

Motivation in second language acquisition in Chinese research students in the UK

The present study sets out to examine motivation to learn English by Chinese research students in an informal setting. Data were collected, using semi-structured interviews, from four research students at two points in time during their first year in the UK. The main findings are: they believed that learning English was important; their main goal orientations were instrumental and extrinsic; they set learning goals and persisted to attain them; they valued their current learning environment in general and saw it as supportive of their goals; they held both positive and negative attitudes towards the British, which had differential effects on their motivation; their self-perceived support seemed to have a positive impact on their motivation and the development of self-confidence; they tended to attribute their success to stable causes such as the environment and failure to unstable but controllable causes such as effort. It is concluded that qualitative data of this kind may complement insights from quantitative research. Implications for target country institutions in the provision of support are discussed.

Strengthening artisanal mining research and policy through baseline census activities

Over the past 10-15 years, several governments have implemented an array of technology, support-related, sustainable livelihoods (SL) and poverty-reduction projects for artisanal and small-scale mining (ASM). In the majority of cases, however, these interventions have failed to facilitate improvements in the industry's productivity and raise the living standards of the sector's subsistence operators. This article argues that a poor understanding of the demographics of target populations has precipitated these outcomes. In order to strengthen policy and assistance in the sector, governments must determine, with greater precision, the number of people operating in ASM regions, their origins and ethnic backgrounds, ages, and educational levels. This can be achieved by carrying out basic and localized census work before promoting ambitious sector-specific projects aimed at improving working conditions in the industry.

The effect of farming system and fertilisers on pests and natural enemies: A synthesis of current research

Organic agriculture is becoming widespread due to increased consumer demand and regulatory and political support. Organic agriculture can increase arthropod diversity but the response of pests and their natural enemies is variable. Fertiliser is an important component of agricultural systems and its effects on pests and natural enemies will influence agroecosystems. In this study, meta-analysis and vote-counting techniques were used to compare farming system (organic and conventional) and fertiliser effects on arthropod pests and their natural enemies. The meta-analyses indicated that pests generally benefitted from organic techniques, this is particularly evident when experiments were carried out on a smaller scale. Pest responses to organic and conventional fertiliser types were divergent, plant composts benefitted pest arthropods while the opposite was true for manures, this has implications for pest management. Most natural enemy groups responded positively to organic farming although this was not true for Coleopterans. Experimental scale had a prominent impact on natural enemy responses with farm scale studies showing particularly positive effects of organic agriculture on natural enemies. This suggests that it is large scale features of organic agriculture such as landscape heterogeneity that are beneficial to natural enemies. Natural enemy responses to organic fertilisers were positive indicating that field scale management practices including fertiliser can also be important in pest management.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Children with Special Educational Needs as film makers: Using digital video to support Understandings of Narrative

The literature has identified issues around transitions among phases for all pupils (Cocklin, 1999) including pupils with special educational needs (SEN) (Morgan 1999, Maras and Aveling 2006). These issues include pupils’ uncertainties and worries about building size and spatial orientation, exposure to a range of teaching styles, relationships with peers and older pupils as well as parents’ difficulties in establishing effective communications with prospective secondary schools. Research has also identified that interventions to facilitate these educational transitions should consider managerial support, social and personal familiarisation with the new setting as well as personalised learning strategies (BECTA 2004). However, the role that digital technologies can play in supporting these strategies or facilitating the role of the professionals such as SENCos and heads of departments involved in supporting effective transitions for pupils with SEN has not been widely discussed. Uses of ICT include passing references of student-produced media presentations (Higgins 1993) and use of photographs of activities attached to a timetable to support familiarisation with the secondary curriculum for pupils with autism (Cumine et al. 1998).

Simulation models in farming systems research : potential and challenges

Integrated simulation models can be useful tools in farming system research. This chapter reviews three commonly used approaches, i.e. linear programming, system dynamics and agent-based models. Applications of each approach are presented and strengths and drawbacks discussed. We argue that, despite some challenges, mainly related to the integration of different approaches, model validation and the representation of human agents, integrated simulation models contribute important insights to the analysis of farming systems. They help unravelling the complex and dynamic interactions and feedbacks among bio-physical, socio-economic, and institutional components across scales and levels in farming systems. In addition, they can provide a platform for integrative research, and can support transdisciplinary research by functioning as learning platforms in participatory processes.

Toward a new generation of world climate research and computing facilities

The impending threat of global climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather prediction and in our understanding and ability to model the climate system suggest that it is both necessary and possible to revolutionize climate prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the climate system, and because the regional manifestations of climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requirements to predict its behavior reliably are so enormous that the nations of the world should create a small number of multinational high-performance computing facilities dedicated to the grand challenges of developing the capabilities to predict climate variability and change on both global and regional scales over the coming decades. Such facilities will play a key role in the development of next-generation climate models, build global capacity in climate research, nurture a highly trained workforce, and engage the global user community, policy-makers, and stakeholders. We recommend the creation of a small number of multinational facilities with computer capability at each facility of about 20 peta-flops in the near term, about 200 petaflops within five years, and 1 exaflop by the end of the next decade. Each facility should have sufficient scientific workforce to develop and maintain the software and data analysis infrastructure. Such facilities will enable questions of what resolution, both horizontal and vertical, in atmospheric and ocean models, is necessary for more confident predictions at the regional and local level. Current limitations in computing power have placed severe limitations on such an investigation, which is now badly needed. These facilities will also provide the world's scientists with the computational laboratories for fundamental research on weather–climate interactions using 1-km resolution models and on atmospheric, terrestrial, cryospheric, and oceanic processes at even finer scales. Each facility should have enabling infrastructure including hardware, software, and data analysis support, and scientific capacity to interact with the national centers and other visitors. This will accelerate our understanding of how the climate system works and how to model it. It will ultimately enable the climate community to provide society with climate predictions, which are based on our best knowledge of science and the most advanced technology.

Social development as knowledge building: research as a sphere of policy influence

The value of using social development knowledge as a tool for building development policy was promoted by the British Department for International Development in the late 1990s. This article takes the case of a capacity building initiative that sought to build social development knowledge as a resource for policy formulation in 'southern' countries. Situating knowledge as a development resource presents difficulties for intervention processes that have historically developed to provide access to economic and social assets. This article highlights some of the issues involved in trying to build social development capacity and questions the suitability of this style of intervention. Inappropriate and short-term support for knowledge capacity building carries the danger that the traditional separation between the academic and practice spheres will be reinforced, making the process of democratising knowledge more difficult.