30 resultados para knowledge based development


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Sustainable development could provide a critical foil for individual
and especially collective reflection on the normative
direction, ends and means employed by societies, particularly
around the economy, its technology and resource-intensive
orientation and configuration with ecosystems. However,
although sustainable development is a constitutional objective
of the EU, its implementation in strategies and policies reveals
a much narrower meaning. By framing sustainable development
as ecological modernisation on the basis of technoscientific
innovation, and by imagining citizens as entrepreneurs in a
knowledge-based European economy, openings for democratic
experimentation and social innovation are limited and even
forestalled. In addition, the disruptive and transformational
potential of citizenship is stymied. Still, sustainable development
has resonance within citizenship and human rights
discourses that provide important resources for the fashioning
of common understanding. These are valuable supplements to
the repertoire of European citizenship that could help to embed
sustainable development in the social fabric and generate
alternative imaginaries and futures of a sustainable Europe.

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Fire has long been recognized as an agent of rock weathering. Our understanding of the impact of fire on stone comes either from early anecdotal evidence, or from more recent laboratory simulation studies, using furnaces to simulate the effects of fire. This paper suggests that knowledge derived from simulated heating experiments is based on the preconceptions of the experiment designer – when using a furnace to simulate fire, the operator decides on the maximum temperature and the duration of the experiment. These are key factors in determining the response of the stone to fire, and if these are removed from realworld observations then knowledge based on these simulations must be questioned. To explore the differences between heating sandstone in a furnace and a real fire, sample blocks of Peakmoor Sandstone were subjected to different stress histories in combination (lime rendering and removal, furnace heating or fire, frost and salt weathering). Block response to furnace heating and fire is discussed, with emphasis placed on the non-uniformity of the fire and of block response to fire in contrast to the uniform response to surface heating in a furnace. Subsequent response to salt weathering (by a 10% solution of sodium chloride and magnesium sulphate) was then monitored by weight loss. Blocks that had experienced fire showed a more unpredictable response to salt weathering than those that had undergone furnace heating – spalling of corners and rapid catastrophic weight loss were evidenced in blocks that had been subjected to fire, after periods of relative quiescence. An important physical side-effect of the fire was soot accumulation, which created a waxy, relatively impermeable layer on some blocks. This layer repelled water and hindered salt ingress, but eventually detached when salt, able to enter the substrate through more permeable areas, concentrated and crystallized behind it, resulting in rapid weight loss and accelerated decay. Copyright ©2007 John Wiley & Sons, Ltd.

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Recent thinking on open innovation and the knowledge-based economy have stressed the importance of external knowledge sources in stimulating innovation. Policy-makers have recognised this, establishing publicly funded Centres of R&D Excellence with the objective of stimulating industry–science links and localised innovation spillovers. Here, we examine the contrasting IP management practices of a group of 18 university- and company-based R&D centres supported by the same regional programme. Our analysis covers all but one of the Centres supported by the programme and suggests marked contrasts between the IP strategies of the university-based and company-based centres. This suggests the potential for very different types of knowledge spillovers from publicly funded R&D centres based in different types of organisations, and a range of alternative policy approaches to the future funding of R&D centres depending on policy-makers’ objectives.

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Knowledge is an important component in many intelligent systems.
Since items of knowledge in a knowledge base can be conflicting, especially if
there are multiple sources contributing to the knowledge in this base, significant
research efforts have been made on developing inconsistency measures for
knowledge bases and on developing merging approaches. Most of these efforts
start with flat knowledge bases. However, in many real-world applications, items
of knowledge are not perceived with equal importance, rather, weights (which
can be used to indicate the importance or priority) are associated with items of
knowledge. Therefore, measuring the inconsistency of a knowledge base with
weighted formulae as well as their merging is an important but difficult task. In
this paper, we derive a numerical characteristic function from each knowledge
base with weighted formulae, based on the Dempster-Shafer theory of evidence.
Using these functions, we are able to measure the inconsistency of the knowledge
base in a convenient and rational way, and are able to merge multiple knowledge
bases with weighted formulae, even if knowledge in these bases may be
inconsistent. Furthermore, by examining whether multiple knowledge bases are
dependent or independent, they can be combined in different ways using their
characteristic functions, which cannot be handled (or at least have never been
considered) in classic knowledge based merging approaches in the literature.

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As the population of most developed countries ages so the prevalence of diseases such as age-related macular degeneration (AMD) are likely to increase. To facilitate planning and informed debate regarding making provisions for this disease it is important that we have a clear understanding of the economic impact of visual impairment associated with AMD. In this paper we assess the state of current knowledge based on a review of published evidence in scientific journals. Based on our assessment of the evidence we argue that the paucity of research studies on the subject and wide variation in estimates produced from the few studies available make it difficult to assess with confidence the likely average direct cost-of-illness associated with AMD. We further argue that significant gaps in our understanding of the costs of AMD (particularly in respect of indirect costs) also exist. Current research should be augmented by more comprehensive studies.

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Use of the Dempster-Shafer (D-S) theory of evidence to deal with uncertainty in knowledge-based systems has been widely addressed. Several AI implementations have been undertaken based on the D-S theory of evidence or the extended theory. But the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major problem. This paper presents an approach to representing such knowledge, in which Yen’s probabilistic multi-set mappings have been extended to evidential mappings, and Shafer’s partition technique is used to get the mass function in a complex evidence space. Then, a new graphic method for describing the knowledge is introduced which is an extension of the graphic model by Lowrance et al. Finally, an extended framework for evidential reasoning systems is specified.

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BACKGROUND:

We have recently identified a number of Quantitative Trait Loci (QTL) contributing to the 2-fold muscle weight difference between the LG/J and SM/J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior (TA) muscle of each strain by RNA-Seq.

RESULTS:

13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG/J and SM/J Regulatory Network (LSRN). The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN (Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10) residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes (QTGs) underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM/J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM/J compared to the LG/J strain (p<0.03).

CONCLUSION:

Thus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.

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Decision making is an important element throughout the life-cycle of large-scale projects. Decisions are critical as they have a direct impact upon the success/outcome of a project and are affected by many factors including the certainty and precision of information. In this paper we present an evidential reasoning framework which applies Dempster-Shafer Theory and its variant Dezert-Smarandache Theory to aid decision makers in making decisions where the knowledge available may be imprecise, conflicting and uncertain. This conceptual framework is novel as natural language based information extraction techniques are utilized in the extraction and estimation of beliefs from diverse textual information sources, rather than assuming these estimations as already given. Furthermore we describe an algorithm to define a set of maximal consistent subsets before fusion occurs in the reasoning framework. This is important as inconsistencies between subsets may produce results which are incorrect/adverse in the decision making process. The proposed framework can be applied to problems involving material selection and a Use Case based in the Engineering domain is presented to illustrate the approach. © 2013 Elsevier B.V. All rights reserved.

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To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.

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Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. This feature makes the model particularly suited for the implementation of classifiers and knowledge-based systems. When working with sets of (instead of single) probability distributions, the identification of the optimal option can be based on different criteria, some of them eventually leading to multiple choices. Yet, most of the inference algorithms for credal nets are designed to compute only the bounds of the posterior probabilities. This prevents some of the existing criteria from being used. To overcome this limitation, we present two simple transformations for credal nets which make it possible to compute decisions based on the maximality and E-admissibility criteria without any modification in the inference algorithms. We also prove that these decision problems have the same complexity of standard inference, being NP^PP-hard for general credal nets and NP-hard for polytrees.

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Fermentation products can chaotropically disorder macromolecular systems and induce oxidative stress, thus inhibiting biofuel production. Recently, the chaotropic activities of ethanol, butanol and vanillin have been quantified (5.93, 37.4, 174kJkg(-1)m(-1) respectively). Use of low temperatures and/or stabilizing (kosmotropic) substances, and other approaches, can reduce, neutralize or circumvent product-chaotropicity. However, there may be limits to the alcohol concentrations that cells can tolerate; e.g. for ethanol tolerance in the most robust Saccharomyces cerevisiae strains, these are close to both the solubility limit (<25%, w/v ethanol) and the water-activity limit of the most xerotolerant strains (0.880). Nevertheless, knowledge-based strategies to mitigate or neutralize chaotropicity could lead to major improvements in rates of product formation and yields, and also therefore in the economics of biofuel production.

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Critical decisions are made by decision-makers throughout
the life-cycle of large-scale projects. These decisions are crucial as they
have a direct impact upon the outcome and the success of projects. To aid
decision-makers in the decision making process we present an evidential
reasoning framework. This approach utilizes the Dezert-Smarandache
theory to fuse heterogeneous evidence sources that suffer from levels
of uncertainty, imprecision and conflicts to provide beliefs for decision
options. To analyze the impact of source reliability and priority upon
the decision making process, a reliability discounting technique and a
priority discounting technique, are applied. A maximal consistent subset
is constructed to aid in dening where discounting should be applied.
Application of the evidential reasoning framework is illustrated using a
case study based in the Aerospace domain.

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