24 resultados para message dissemination

em Aston University Research Archive


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Large-scale evacuations are a recurring theme on news channels, whether in response to major natural or manmade disasters. The role of warning dissemination is a key part in the success of such large-scale evacuations and its inadequacy in certain cases has been a 'primary contribution to deaths and injuries' (Hayden et al.; 2007). Along with technology-driven 'official warning channels' (e.g. sirens, mass media), the role of unofficial channel (e.g. neighbours, personal contacts, volunteer wardens) has proven to be significant in warning the public of the need to evacuate. Although post-evacuation studies identify the behaviours of evacuees as disseminators of the warning message, there has not been a detailed study that quantifies the effects of such behaviour on the warning message dissemination. This paper develops an Agent-Based Simulation (ABS) model of multiple agents (evacuee households) in a hypothetical community to investigate the impact of behaviour as an unofficial channel on the overall warning dissemination. Parameters studied include the percentage of people who warn their neighbours, the efficiency of different official warning channels, and delay time to warn neighbours. Even with a low proportion of people willing to warn their neighbour, the results showed considerable impact on the overall warning dissemination. © 2012 Elsevier B.V. All rights reserved.

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Timely warning of the public during large scale emergencies is essential to ensure safety and save lives. This ongoing study proposes an agent-based simulation model to simulate the warning message dissemination among the public considering both official channels and unofficial channels The proposed model was developed in NetLogo software for a hypothetical area, and requires input parameters such as effectiveness of each official source (%), estimated time to begin informing others, estimated time to inform others and estimated percentage of people (who do not relay the message). This paper demonstrates a means of factoring the behaviour of the public as informants into estimating the effectiveness of warningdissemination during large scale emergencies. The model provides a tool for the practitioner to test the potential impact of the informal channels on the overall warning time and sensitivity of the modelling parameters. The tool would help the practitioners to persuade evacuees to disseminate the warning message informing others similar to the ’Run to thy neighbour campaign conducted by the Red cross.

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An improved inference method for densely connected systems is presented. The approach is based on passing condensed messages between variables, representing macroscopic averages of microscopic messages. We extend previous work that showed promising results in cases where the solution space is contiguous to cases where fragmentation occurs. We apply the method to the signal detection problem of Code Division Multiple Access (CDMA) for demonstrating its potential. A highly efficient practical algorithm is also derived on the basis of insight gained from the analysis. © EDP Sciences.

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The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

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This chapter examines the contexts in which people will process more deeply, and therefore be more influenced by, a position that is supported by either a numerical majority or minority. The chapter reviews the major theories of majority and minority influence with reference to which source condition is associated with most message processing (and where relevant, the contexts under which this occurs) and experimental research examining these predictions. The chapter then presents a new theoretical model (the source-context-elaboration model, SCEM) that aims to integrate the disparate research findings. The model specifies the processes underlying majority and minority influence, the contexts under which these processes occur and the consequences for attitudes changed by majority and minority influence. The chapter then describes a series of experiments that address each of the aspects of the theoretical model. Finally, a range of research-related issues are discussed and future issues for the research area as a whole are considered.

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Two experiments examined the extent to which attitudes changed following majority and minority influence are resistant to counter-persuasion. In both experiments participants' attitudes were measured after being exposed to two messages, delayed in time, which argued opposite positions (initial message and counter-message). In the first experiment, attitudes following minority endorsement of the initial message were more resistant to a second counter-message only when the initial message contained strong versus weak arguments. Attitudes changed following majority influence did not resist the second counter-message and returned to their pre-test level. Experiment 2 varied whether memory was warned (i.e., message recipients expected to recall the message) or not, to manipulate message processing. When memory was warned, which should increase message processing, attitudes changed following both majority and minority influence resisted the second counter-message. The results support the view that minority influence instigates systematic processing of its arguments, leading to attitudes that resist counter-persuasion. Attitudes formed following majority influence yield to counter-persuasion unless there is a secondary task that encourages message processing.

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Two experiments are reported that examine the effects of caffeine consumption on attitude change by using different secondary tasks to manipulate message processing. The first experiment employed an orientating task whilst the second experiment employed a distracter task. In both experiments participants consumed an orange-juice drink that either contained caffeine (3.5?mg/kg body weight) or did not contain caffeine (placebo) prior to reading a counter-attitudinal communication. The results across both experiments were similar. When message processing was reduced or under high distraction, there was no attitude change irrespective of caffeine consumption. However, when message processing was enhanced or under low distraction, there was greater attitude change in the caffeine vs. placebo conditions. Furthermore, attitudes formed after caffeine consumption resisted counter-persuasion (Experiment 1) and led to indirect attitude change (Experiment 2). The extent that participants engaged in message-congruent thinking mediated the amount of attitude change. These results provide evidence that moderate amounts of caffeine increase systematic processing of the arguments in the message resulting in greater agreement.

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Two experiments investigated the conditions under which majority and minority sources instigate systematic processing of their messages. Both experiments crossed source status (majority vs. minority) with message quality (strong vs. weak arguments). In each experiment, message elaboration was manipulated by varying either motivational (outcome relevance, Experiment 1) or cognitive (orientating tasks, Experiment 2) factors. The results showed that when either motivational or cognitive factors encouraged low message elaboration, there was heuristic acceptance of the majority position without detailed message processing. When the level of message elaboration was intermediate, there was message processing only for the minority source. Finally, when message elaboration was high, there was message processing for both source conditions. These results show that majority and minority influence is sensitive to motivational and cognitive factors that constrain or enhance message elaboration and that both sources can lead to systematic processing under specific circumstances. © 2007 by the Society for Personality and Social Psychology, Inc.

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Initially this thesis examines the various mechanisms by which technology is acquired within anodizing plants. In so doing the history of the evolution of anodizing technology is recorded, with particular reference to the growth of major markets and to the contribution of the marketing efforts of the aluminium industry. The business economics of various types of anodizing plants are analyzed. Consideration is also given to the impact of developments in anodizing technology on production economics and market growth. The economic costs associated with work rejected for process defects are considered. Recent changes in the industry have created conditions whereby information technology has a potentially important role to play in retaining existing knowledge. One such contribution is exemplified by the expert system which has been developed for the identification of anodizing process defects. Instead of using a "rule-based" expert system, a commercial neural networks program has been adapted for the task. The advantages of neural networks over 'rule-based' systems is that they are better suited to production problems, since the actual conditions prevailing when the defect was produced are often not known with certainty. In using the expert system, the user first identifies the process stage at which the defect probably occurred and is then directed to a file enabling the actual defects to be identified. After making this identification, the user can consult a database which gives a more detailed description of the defect, advises on remedial action and provides a bibliography of papers relating to the defect. The database uses a proprietary hypertext program, which also provides rapid cross-referencing to similar types of defect. Additionally, a graphics file can be accessed which (where appropriate) will display a graphic of the defect on screen. A total of 117 defects are included, together with 221 literature references, supplemented by 48 cross-reference hyperlinks. The main text of the thesis contains 179 literature references. (DX186565)

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Intelligent transport system (ITS) has large potentials on road safety applications as well as nonsafety applications. One of the big challenges for ITS is on the reliable and cost-effective vehicle communications due to the large quantity of vehicles, high mobility, and bursty traffic from the safety and non-safety applications. In this paper, we investigate the use of dedicated short-range communications (DSRC) for coexisting safety and non-safety applications over infrastructured vehicle networks. The main objective of this work is to improve the scalability of communications for vehicles networks, ensure QoS for safety applications, and leave as much as possible bandwidth for non-safety applications. A two-level adaptive control scheme is proposed to find appropriate message rate and control channel interval for safety applications. Simulation results demonstrated that this adaptive method outperforms the fixed control method under varying number of vehicles. © 2012 Wenyang Guan et al.

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Quality of services (QoS) support is critical for dedicated short range communications (DSRC) vehicle networks based collaborative road safety applications. In this paper we propose an adaptive power and message rate control method for DSRC vehicle networks at road intersections. The design objective is to provide high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method an offline simulation based approach is used to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network. The identified best configurations are then used online by roadside access points (AP) according to estimated number of vehicles. Simulation results show that this adaptive method significantly outperforms a fixed control method. © 2011 Springer-Verlag.

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Cognitive Radio has been proposed as a key technology to significantly improve spectrum usage in wireless networks by enabling unlicensed users to access unused resource. We present new algorithms that are needed for the implementation of opportunistic scheduling policies that maximize the throughput utilization of resources by secondary users, under maximum interference constraints imposed by existing primary users. Our approach is based on the Belief Propagation (BP) algorithm, which is advantageous due to its simplicity and potential for distributed implementation. We examine convergence properties and evaluate the performance of the proposed BP algorithms via simulations and demonstrate that the results compare favorably with a benchmark greedy strategy. © 2013 IEEE.