An Agent Based Education Resource Purvey System

Most current education organizations use books and CDs as the main media, which takes a long time for knowledge updating between education resource providers and the users. The rapid development of the Internet has brought with it the possibility of improving the resource purveying mechanisms. Therefore, we designed an agent based system to purvey education resources from the resource centre to schools through the Internet. Agent technology helps to improve system performance and flexibility. This paper describes the design of our system, details the functions of the main parts of the system, shows the communication methods between agents and finally evaluates the system by experiments.

A Mobile Agent Based Spam Filter System

A new distributed spam filter system based on mobile agent is proposed in this paper. We introduce the application of mobile agent technology to the spam filter system. The system architecture, the work process, the pivotal technology of the distributed spam filter system based on mobile agent, and the Naive Bayesian filter method are described in detail. The experiment results indicate that the system can prevent spam emails effectively.

A multi-agent architect for CSCW systems: from organisational semiotics perspective

This paper describes a multi-agent architecture to support CSCW systems modelling. Since CSCW involves different organizations, it can be seen as a social model. From this point of view, we investigate the possibility of modelling CSCW by agent technology, and then based on organizational semiotics method a multi-agent architecture is proposed via using EDA agent model. We explain the components of this multi-agent architecture and design process. It is argued that this approach provides a new perspective for modelling CSCW systems.

ACC2 gene polymorphisms, metabolic syndrome, and gene-nutrient interactions with dietary fat.

Acetyl-CoA carboxylase β (ACC2) plays a key role in fatty acid synthesis and oxidation pathways. Disturbance of these pathways is associated with impaired insulin responsiveness and metabolic syndrome (MetS). Gene-nutrient interactions may affect MetS risk. This study determined the relationship between ACC2 polymorphisms (rs2075263, rs2268387, rs2284685, rs2284689, rs2300453, rs3742023, rs3742026, rs4766587, and rs6606697) and MetS risk, and whether dietary fatty acids modulate this in the LIPGENE-SU.VI.MAX study of MetS cases and matched controls (n = 1754). Minor A allele carriers of rs4766587 had increased MetS risk (OR 1.29 [CI 1.08, 1.58], P = 0.0064) compared with the GG homozygotes, which may in part be explained by their increased body mass index (BMI), abdominal obesity, and impaired insulin sensitivity (P < 0.05). MetS risk was modulated by dietary fat intake (P = 0.04 for gene-nutrient interaction), where risk conferred by the A allele was exacerbated among individuals with a high-fat intake (>35% energy) (OR 1.62 [CI 1.05, 2.50], P = 0.027), particularly a high intake (>5.5% energy) of n-6 polyunsaturated fat (PUFA) (OR 1.82 [CI 1.14, 2.94], P = 0.01; P = 0.05 for gene-nutrient interaction). Saturated and monounsaturated fat intake did not modulate MetS risk. Importantly, we replicated some of these findings in an independent cohort. In conclusion, the ACC2 rs4766587 polymorphism influences MetS risk, which was modulated by dietary fat, suggesting novel gene-nutrient interactions.

A nutrient increment method for reducing bias in global biogeochemical models

Assimilation of physical variables into coupled physical/biogeochemical models poses considerable difficulties. One problem is that data assimilation can break relationships between physical and biological variables. As a consequence, biological tracers, especially nutrients, are incorrectly displaced in the vertical, resulting in unrealistic biogeochemical fields. To prevent this, we present the idea of applying an increment to the nutrient field within a data assimilating model to ensure that nutrient-potential density relationships are maintained within a water column during assimilation. After correcting the nutrients, it is assumed that other biological variables rapidly adjust to the corrected nutrient fields. We applied this method to a 17 year run of the 2° NEMO ocean-ice model coupled to the PlankTOM5 ecosystem model. Results were compared with a control with no assimilation, and with a model with physical assimilation but no nutrient increment. In the nutrient incrementing experiment, phosphate distributions were improved both at high latitudes and at the equator. At midlatitudes, assimilation generated unrealistic advective upwelling of nutrients within the boundary currents, which spread into the subtropical gyres resulting in more biased nutrient fields. This result was largely unaffected by the nutrient increment and is probably due to boundary currents being poorly resolved in a 2° model. Changes to nutrient distributions fed through into other biological parameters altering primary production, air-sea CO2 flux, and chlorophyll distributions. These secondary changes were most pronounced in the subtropical gyres and at the equator, which are more nutrient limited than high latitudes.

Gene-nutrient interactions with dietary fat modulate the association between genetic variation of the ACSL1 gene and metabolic syndrome

Long-chain acyl CoA synthetase 1 (ACSL1) plays an important role in fatty acid metabolism and triacylglycerol (TAG) synthesis. Disturbance of these pathways may result in dyslipidemia and insulin resistance, hallmarks of the metabolic syndrome (MetS). Dietary fat is a key environmental factor that may interact with genetic determinants of lipid metabolism to affect MetS risk. We investigated the relationship between ACSL1 polymorphisms (rs4862417, rs6552828, rs13120078, rs9997745, and rs12503643) and MetS risk and determined potential interactions with dietary fat in the LIPGENE-SU.VI.MAX study of MetS cases and matched controls (n = 1,754). GG homozygotes for rs9997745 had increased MetS risk {odds ratio (OR) 1.90 [confidence interval (CI) 1.15, 3.13]; P = 0.01}, displayed elevated fasting glucose (P = 0.001) and insulin concentrations (P = 0.002) and increased insulin resistance (P = 0.03) relative to the A allele carriers. MetS risk was modulated by dietary fat, whereby the risk conferred by GG homozygosity was abolished among individuals consuming either a low-fat (<35% energy) or a high-PUFA diet (>5.5% energy). In conclusion, ACSL1 rs9997745 influences MetS risk, most likely via disturbances in fatty acid metabolism, which was modulated by dietary fat consumption, particularly PUFA intake, suggesting novel gene-nutrient interactions.

Intelligent agents for fault tolerance: from multi-agent simulation to cluster-based implementation

Recent research in multi-agent systems incorporate fault tolerance concepts, but does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely 'Intelligent Agents'. A task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The feasibility of the approach is validated by simulations on an FPGA using a multi-agent simulator, and implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

Space partitioning for scalable K-means

K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional binary search trees (KD-Trees). A combination of an efficient data structure and geometrical constraints allow to reduce the number of distance computations required at each iteration. In this work we present a general space partitioning approach for improving the efficiency and the scalability of the K-Means algorithm. We propose to adopt approximate hierarchical clustering methods to generate binary space partitioning trees in contrast to KD-Trees. In the experimental analysis, we have tested the performance of the proposed Binary Space Partitioning K-Means (BSP-KM) when a divisive clustering algorithm is used. We have carried out extensive experimental tests to compare the proposed approach to the one based on KD-Trees (KD-KM) in a wide range of the parameters space. BSP-KM is more scalable than KDKM, while keeping the deterministic nature of the `brute force' algorithm. In particular, the proposed space partitioning approach has shown to overcome the well-known limitation of KD-Trees in high-dimensional spaces and can also be adopted to improve the efficiency of other algorithms in which KD-Trees have been used.