Using HLT for acquiring, retrieving and publishing knowledge in AKT:position paper

AKT is a major research project applying a variety of technologies to knowledge management. Knowledge is a dynamic, ubiquitous resource, which is to be found equally in an expert's head, under terabytes of data, or explicitly stated in manuals. AKT will extend knowledge management technologies to exploit the potential of the semantic web, covering the use of knowledge over its entire lifecycle, from acquisition to maintenance and deletion. In this paper we discuss how HLT will be used in AKT and how the use of HLT will affect different areas of KM, such as knowledge acquisition, retrieval and publishing.

Noise modelling and outlier detection in satellite scatterometer data

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A novel approach to modelling and exploiting uncertainty in stochastic control systems

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

Activation of protein kinase B by adenosine A1 and A3 receptors in newborn rat cardiomyocytes

It is well established that adenosine receptors are involved in cardioprotection and that protein kinase B (PKB) is associated with cell survival. Therefore, in this study we have investigated whether adenosine receptors (A1, A2A and A3) activate PKB by Western blotting and determined the involvement of phosphatidylinositol 3-kinase (PI-3K)/PKB in adenosine-induced preconditioning in cultured newborn rat cardiomyocytes. Adenosine (non-selective agonist), CPA (A1 selective agonist) and Cl-IB-MECA (A(3) selective agonist) all increased PKB phosphorylation in a time- and concentration-dependent manner. The combined maximal response to CPA and Cl-IB-MECA was similar to the increase in PKB phosphorylation induced by adenosine alone. CGS 21680 (A2A selective agonist) did not stimulate an increase in PKB phosphorylation. Adenosine, CPA and Cl-IB-MECA-mediated PKB phosphorylation were inhibited by pertussis toxin (PTX blocks G(i)/G(o)-protein), genistein (tyrosine kinase inhibitor), PP2 (Src tyrosine kinase inhibitor) and by the epidermal growth factor (EGF) receptor tyrosine kinase inhibitor AG 1478. The PI-3K inhibitors wortmannin and LY 294002 blocked A(1) and A(3) receptor-mediated PKB phosphorylation. The role of PI-3K/PKB in adenosine-induced preconditioning was assessed by monitoring Caspase 3 activity and lactate dehydrogenase (LDH) release induced by exposure of cardiomyocytes to 4 h hypoxia (0.5% O2) followed by 18 h reoxygenation (HX4/R). Pre-treatment with wortmannin had no significant effect on the ability of adenosine-induced preconditioning to reduce the release of LDH or Caspase 3 activation following HX4/R. In conclusion, we have shown for the first time that adenosine A1 and A3 receptors trigger increases in PKB phosphorylation in rat cardiomyocytes via a G1/G0-protein and tyrosine kinase-dependent pathway. However, the PI-3K/PKB pathway does not appear to be involved in adenosine-induced cardioprotection by preconditioning Adenosine A1 receptor .

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Modelling and linking transformations in EPCIS governing supply chain business processes

Supply chains comprise of complex processes spanning across multiple trading partners. The various operations involved generate large number of events that need to be integrated in order to enable internal and external traceability. Further, provenance of artifacts and agents involved in the supply chain operations is now a key traceability requirement. In this paper we propose a Semantic web/Linked data powered framework for the event based representation and analysis of supply chain activities governed by the EPCIS specification. We specifically show how a new EPCIS event type called "Transformation Event" can be semantically annotated using EEM - The EPCIS Event Model to generate linked data, that can be exploited for internal event based traceability in supply chains involving transformation of products. For integrating provenance with traceability, we propose a mapping from EEM to PROV-O. We exemplify our approach on an abstraction of the production processes that are part of the wine supply chain.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

Productivity growth and efficiency measurements in fuzzy environments with an application to health care

Health care organizations must continuously improve their productivity to sustain long-term growth and profitability. Sustainable productivity performance is mostly assumed to be a natural outcome of successful health care management. Data envelopment analysis (DEA) is a popular mathematical programming method for comparing the inputs and outputs of a set of homogenous decision making units (DMUs) by evaluating their relative efficiency. The Malmquist productivity index (MPI) is widely used for productivity analysis by relying on constructing a best practice frontier and calculating the relative performance of a DMU for different time periods. The conventional DEA requires accurate and crisp data to calculate the MPI. However, the real-world data are often imprecise and vague. In this study, the authors propose a novel productivity measurement approach in fuzzy environments with MPI. An application of the proposed approach in health care is presented to demonstrate the simplicity and efficacy of the procedures and algorithms in a hospital efficiency study conducted for a State Office of Inspector General in the United States. © 2012, IGI Global.

Plant oils as fuels for compression ignition engines: a technical review and life-cycle analysis

As an alternative fuel for compression ignition engines, plant oils are in principle renewable and carbon-neutral. However, their use raises technical, economic and environmental issues. A comprehensive and up-to-date technical review of using both edible and non-edible plant oils (either pure or as blends with fossil diesel) in CI engines, based on comparisons with standard diesel fuel, has been carried out. The properties of several plant oils, and the results of engine tests using them, are reviewed based on the literature. Findings regarding engine performance, exhaust emissions and engine durability are collated. The causes of technical problems arising from the use of various oils are discussed, as are the modifications to oil and engine employed to alleviate these problems. The review shows that a number of plant oils can be used satisfactorily in CI engines, without transesterification, by preheating the oil and/or modifying the engine parameters and the maintenance schedule. As regards life-cycle energy and greenhouse gas emission analyses, these reveal considerable advantages of raw plant oils over fossil diesel and biodiesel. Typical results show that the life-cycle output-to-input energy ratio of raw plant oil is around 6 times higher than fossil diesel. Depending on either primary energy or fossil energy requirements, the life-cycle energy ratio of raw plant oil is in the range of 2–6 times higher than corresponding biodiesel. Moreover, raw plant oil has the highest potential of reducing life-cycle GHG emissions as compared to biodiesel and fossil diesel.