27 resultados para Overlapping consensus


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A method for direct inscription of fibre Bragg gratings laterally separated by inscription in separate segments of the fibre core is demonstrated for the first time.

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IEEE 802.15.4 standard is a relatively new standard designed for low power low data rate wireless sensor networks (WSN), which has a wide range of applications, e.g., environment monitoring, e-health, home and industry automation. In this paper, we investigate the problems of hidden devices in coverage overlapped IEEE 802.15.4 WSNs, which is likely to arise when multiple 802.15.4 WSNs are deployed closely and independently. We consider a typical scenario of two 802.15.4 WSNs with partial coverage overlapping and propose a Markov-chain based analytical model to reveal the performance degradation due to the hidden devices from the coverage overlapping. Impacts of the hidden devices and network sleeping modes on saturated throughput and energy consumption are modeled. The analytic model is verified by simulations, which can provide the insights to network design and planning when multiple 802.15.4 WSNs are deployed closely. © 2013 IEEE.

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The purpose of this piece is to explain how the trust concept fits the overlapping analysis, presenting an example of why discrete categorisation is often unhelpful in understanding the operation of legal concepts.

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We have examined the statistics of simulated bit-error rates in optical transmission systems with strong patterning effects and have found strong correlation between the probability of marks in a pseudorandom pattern and the error-free transmission distance. We discuss how a reduced density of marks can be achieved by preencoding optical data.

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Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.

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When facing a crisis, leaders' sensemaking can take a considerable amount of time due to the need to develop consensus in how to deal with it so that vision formation and sensegiving can take place. However, research into emerging cognitive consensus when leaders deal with a crisis over time is lacking. This is limiting a detailed understanding of how organizations respond to crises. The findings, based on a longitudinal analysis of cognitive maps within three management teams at a single organization, highlight considerable individual differences in cognitive content when starting to make sense of a crisis. Evidence for an emerging viable prescriptive mental model for the future was found, but not so much in the management as a whole. Instead, the findings highlight increasing cognitive consensus based on similarities in objectives and cause-effect beliefs within well-defined management teams over time.

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The thesis presented an overlapping analysis of private law institutions, in response to the arguments that law must be separated into discrete categories. The basis of this overlapping approach was the realist perspective, which emphasises the role of facts and outcomes as the starting point for legal analysis as opposed to legal principle or doctrine.

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We propose a novel architecture for all-optical add-drop multiplexing of OFDM signals. Sub-channel extraction is achieved by means of waveform replication and coherent subtraction from the OFDM super-channel. Numerical simulations have been carried out to benchmark the performance of the architecture against critical design parameters.

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Recent modelling studies (Hadjipapas et al. [2009]: Neuroimage 44:1290-1303) have shown that it may be possible to distinguish between different neuronal populations on the basis of their macroscopically measured (EEG/MEG) mean field. We set out to test whether the different orientation columns contributing to a signal at a specific cortical location could be identified based on the measured MEG signal. We used 1.5deg square, static, obliquely oriented grating stimuli to generate sustained gamma oscillations in a focal region of primary visual cortex. We then used multivariate classifier methods to predict the orientation (left or right oblique) of the stimuli based purely on the time-series data from this one location. Both the single trial evoked response (0-300 ms) and induced post-transient power spectra (300-2,300 ms, 20-70 Hz band) due to the different stimuli were classifiable significantly above chance in 11/12 and 10/12 datasets respectively. Interestingly, stimulus-specific information is preserved in the sustained part of the gamma oscillation, long after perception has occurred and all neuronal transients have decayed. Importantly, the classification of this induced oscillation was still possible even when the power spectra were rank-transformed showing that the different underlying networks give rise to different characteristic temporal signatures. © 2009 Wiley-Liss, Inc.

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This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.

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Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies, and the corporate and nongovernmental organisation (NGO) sectors, in this consensus report, we analyse, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathway-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this.