333 resultados para tweet seats


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Twitter has become a major instrument for the rapid dissemination and subsequent debate of news stories. It has been instrumental both in drawing attention to events as they unfolded (such as the emergency landing of a plane in New York’s Hudson River in 2009) and in facilitating a sustained discussion of major stories over timeframes measured in weeks and months (including the continuing saga around Wikileaks and Julian Assange), sometimes still keeping stories alive even if mainstream media attention has moved on elsewhere. More comprehensive methodologies for research into news discussion on Twitter – beyond anecdotal or case study approaches – are only now beginning to emerge. This paper presents a large-scale quantitative approach to studying public communication in the Australian Twittersphere, developed as part of a three-year ARC Discovery project that also examines blogs and other social media spaces. The paper will both outline the innovative research tools developed for this work, and present outcomes from an application of these methodologies to recent and present news themes. Our methodology enables us to identify major themes in Twitter’s discussion of these events, trace their development and decline over time, and map the dynamics of the discussion networks formed ad hoc around specific themes (in part with the help of Twitter #hashtags: brief identifiers which mark a tweet as taking part in an established discussion). It is also able to identify links to major news stories and other online resources, and to track their dissemination across the wider Twittersphere.

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Harry’s is my favourite bar in my neighbourhood. It is a small wine bar, owned by three men in their late thirties and targeted at people like them; my gentrifying inner city neighbourhood’s 20 to 40 something urban middle class. Harry’s has seats along the bar, booths inside, and a courtyard out the back. The seating arrangements mean that larger groups tend to gather outside, groups of two to four spread around the location, and people by themselves, or in groups of two, tend to sit at the bar. I usually sit at the bar....

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The compressed gas industry and government agencies worldwide utilize "adiabatic compression" testing for qualifying high-pressure valves, regulators, and other related flow control equipment for gaseous oxygen service. This test methodology is known by various terms including adiabatic compression testing, gaseous fluid impact testing, pneumatic impact testing, and BAM testing as the most common terms. The test methodology will be described in greater detail throughout this document but in summary it consists of pressurizing a test article (valve, regulator, etc.) with gaseous oxygen within 15 to 20 milliseconds (ms). Because the driven gas1 and the driving gas2 are rapidly compressed to the final test pressure at the inlet of the test article, they are rapidly heated by the sudden increase in pressure to sufficient temperatures (thermal energies) to sometimes result in ignition of the nonmetallic materials (seals and seats) used within the test article. In general, the more rapid the compression process the more "adiabatic" the pressure surge is presumed to be and the more like an isentropic process the pressure surge has been argued to simulate. Generally speaking, adiabatic compression is widely considered the most efficient ignition mechanism for directly kindling a nonmetallic material in gaseous oxygen and has been implicated in many fire investigations. Because of the ease of ignition of many nonmetallic materials by this heating mechanism, many industry standards prescribe this testing. However, the results between various laboratories conducting the testing have not always been consistent. Research into the test method indicated that the thermal profile achieved (i.e., temperature/time history of the gas) during adiabatic compression testing as required by the prevailing industry standards has not been fully modeled or empirically verified, although attempts have been made. This research evaluated the following questions: 1) Can the rapid compression process required by the industry standards be thermodynamically and fluid dynamically modeled so that predictions of the thermal profiles be made, 2) Can the thermal profiles produced by the rapid compression process be measured in order to validate the thermodynamic and fluid dynamic models; and, estimate the severity of the test, and, 3) Can controlling parameters be recommended so that new guidelines may be established for the industry standards to resolve inconsistencies between various test laboratories conducting tests according to the present standards?

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Seat pressure is known as a major factor of seat comfort in vehicles. In passenger vehicles, there is lacking research into the seat comfort of rear seat occupants. As accurate seat pressure measurement requires significant effort, simulation of seat pressure is evolving as a preferred method. However, analytic methods are based on complex finite element modeling and therefore are time consuming and involve high investment. Based on accurate anthropometric measurements of 64 male subjects and outboard rear seat pressure measurements in three different passenger vehicles, this study investigates if a set of parameters derived from seat pressure mapping are sensitive enough to differentiate between different seats and whether they correlate with anthropometry in linear models. In addition to the pressure map analysis, H-Points were measured with a coordinate measurement system based on palpated body landmarks and the range of H-Point locations in the three seats is provided. It was found that for the cushion, cushion contact area and cushion front area/force could be modeled by subject anthropometry,while only seatback contact area could be modeled based on anthropometry for all three vehicles. Major differences were found between the vehicles for other parameters.

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Finite element analyses of the human body in seated postures requires digital models capable of providing accurate and precise prediction of the tissue-level response of the body in the seated posture. To achieve such models, the human anatomy must be represented with high fidelity. This information can readily be defined using medical imaging techniques such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Current practices for constructing digital human models, based on the magnetic resonance (MR) images, in a lying down (supine) posture have reduced the error in the geometric representation of human anatomy relative to reconstructions based on data from cadaveric studies. Nonetheless, the significant differences between seated and supine postures in segment orientation, soft-tissue deformation and soft tissue strain create a need for data obtained in postures more similar to the application posture. In this study, we present a novel method for creating digital human models based on seated MR data. An adult-male volunteer was scanned in a simulated driving posture using a FONAR 0.6T upright MRI scanner with a T1 scanning protocol. To compensate for unavoidable image distortion near the edges of the study, images of the same anatomical structures were obtained in transverse and sagittal planes. Combinations of transverse and sagittal images were used to reconstruct the major anatomical features from the buttocks through the knees, including bone, muscle and fat tissue perimeters, using Solidworks® software. For each MR image, B-splines were created as contours for the anatomical structures of interest, and LOFT commands were used to interpolate between the generated Bsplines. The reconstruction of the pelvis, from MR data, was enhanced by the use of a template model generated in previous work CT images. A non-rigid registration algorithm was used to fit the pelvis template into the MR data. Additionally, MR image processing was conducted to both the left and the right sides of the model due to the intended asymmetric posture of the volunteer during the MR measurements. The presented subject-specific, three-dimensional model of the buttocks and thighs will add value to optimisation cycles in automotive seat development when used in simulating human interaction with automotive seats.

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Twitter has become a major instrument for the rapid dissemination and subsequent debate of news stories, and comprehensive methodologies for systematic research into news discussion on Twitter are beginning to emerge. This paper outlines innovative approaches for large-scale quantitative research into how Twitter is used to discuss and cover the news, focusing especially on #hashtags: brief identifiers which mark a tweet as taking part in an established discussion.

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Building on innovative frameworks for analysing and visualising the tweet data available from Twitter, developed by the authors, this paper examines the patterns of tweeting activity that occurred during and after the Feb, 2011 Christchurch earthquake. Local and global responses to the disaster were organised around the pre-existing hashtag #eqnz, which averaged some 100 tweets per minute in the hours following the earthquake. The paper identifies the key contributors to the #eqnz network and shows the key themes of their messages. Emerging from this analysis is a more detailed understanding of Twitter and other social media as key elements in the overall ecology of the media forms used for crisis communication. Such uses point both to the importance of social media as a tool for affected communities to self-organise their disaster response and recovery activities, and as a tool for emergency management services to disseminate key information and receive updates from local communities.

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INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2011 evaluation campaign, which consisted of a five active tracks: Books and Social Search, Data Centric, Question Answering, Relevance Feedback, and Snippet Retrieval. INEX 2011 saw a range of new tasks and tracks, such as Social Book Search, Faceted Search, Snippet Retrieval, and Tweet Contextualization.

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As part of an evaluation of the 2010 legislation for child vehicle occupants in Queensland, road-side observations of private passenger vehicles were used to estimate the proportions of children 0-under 7 years travelling in each of the 5 different restraint types (eg. forward facing child restraint). Data was collected in 4 major population centres: Brisbane, Sunshine Coast, Mackay and Townsville. Almost all children were restrained (95.1%, 95% CI 94.3-95.9%), with only 3.3% (95% CI 2.6-4.0%) clearly unrestrained and 44 (1.6%, 95% CI 1.1-2.1%) for whom restraint status could not be determined (‘unknown’). However, around 24.0% (95 CI 21.8-26.2%) of the target-aged children were deemed inappropriately restrained, primarily comprised of 3-6 year olds in seatbelts (18.7% of the 0-6 year olds, 95% CI 16.3-21.1%) or unrestrained (3.7% of the 0-6 year olds, 95% CI 2.5-4.9%) instead of booster seats. In addition, compliance appeared significantly lower for some regional locations where the proportion of children observed as completely unrestrained was relatively high and of concern

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Road trauma is a leading cause of child injury worldwide. In highly motorised countries, injury as a passenger represents a major proportion of all child road deaths and hospitalisations. Australia is no exception, particularly since there are high levels of private motor vehicle travel to school in most Australian states. Recently the legislation governing the type of car restraints required for children aged under 7 years has changed in Australia, aligning requirements better with accepted best practice. However, it is unclear what effect these changes have had on children’s seating positions or the types of restraints used. A mixed methods evaluation of the impact of the new legislation on compliance was conducted at three times: baseline (Time 1); after announcement that changes were going to be implemented but before enforcement began (Time 2); and after enforcement commenced (Time 3). Measures of compliance were obtained using two methods: road-side observations of vehicles with child passengers; and parental self-report (intercept interviews conducted at Time 2 and Time 3 only). Results from the observations suggested an overall positive effect. Proportions of children occupying front seats decreased overall and use of dedicated child seats increased to almost 40% of the observed children by Time 3. However, almost a quarter of the children observed still occupied front seats. These results differed from those of the interview study where almost no children were reported as usually travelling in the front seat, and reported use of dedicated restraints with children was almost 90%, over twice that of the observations.

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Big data is big news in almost every sector including crisis communication. However, not everyone has access to big data and even if we have access to big data, we often do not have necessary tools to analyze and cross reference such a large data set. Therefore this paper looks at patterns in small data sets that we have ability to collect with our current tools to understand if we can find actionable information from what we already have. We have analyzed 164390 tweets collected during 2011 earthquake to find out what type of location specific information people mention in their tweet and when do they talk about that. Based on our analysis we find that even a small data set that has far less data than a big data set can be useful to find priority disaster specific areas quickly.

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Road trauma is a leading cause of child injury worldwide and in highly motorised countries, injury as a passenger represents a major proportion of all child road deaths and hospitalisations. Australia is no exception, particularly since motorised transport to school is at high levels in most Australian states. Recently the legislation governing the type of car restraints required for children aged under 7 years has changed in most Australian states aligning requirements better with accepted best practice. However, it is unclear what effect these changes have had on children’s seating positions or the types of restraints used. A mixed methods evaluation of the impact of the new legislation on compliance was conducted at three times: baseline (Time 1); after announcement that changes were going to be implemented but before enforcement began (Time 2); and after enforcement commenced (Time 3). Measures of compliance were obtained using two methods: road-side observations of vehicles with child passengers; and parental self-report (intercept interviews conducted at Time 2 and Time 3 only). Results from the observations suggested an overall positive effect. Proportions of children occupying front seats decreased overall and use of dedicated child seas increased to almost 40% of the observed children by Time 3. However, almost a quarter of the children observed were still occupying the front seat. These results were very different from those of the interview study where almost no children were reported as usually travelling in the front seat, and the reported use of dedicated restraints with children was almost 90%, more than twice that in the observations.

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The ability to identify and assess user engagement with transmedia productions is vital to the success of individual projects and the sustainability of this mode of media production as a whole. It is essential that industry players have access to tools and methodologies that offer the most complete and accurate picture of how audiences/users engage with their productions and which assets generate the most valuable returns of investment. Drawing upon research conducted with Hoodlum Entertainment, a Brisbane-based transmedia producer, this project involved an initial assessment of the way engagement tends to be understood, why standard web analytics tools are ill-suited to measuring it, how a customised tool could offer solutions, and why this question of measuring engagement is so vital to the future of transmedia as a sustainable industry. Working with data provided by Hoodlum Entertainment and Foxtel Marketing, the outcome of the study was a prototype for a custom data visualisation tool that allowed access, manipulation and presentation of user engagement data, both historic and predictive. The prototyped interfaces demonstrate how the visualization tool would collect and organise data specific to multiplatform projects by aggregating data across a number of platform reporting tools. Such a tool is designed to encompass not only platforms developed by the transmedia producer but also sites developed by fans. This visualisation tool accounted for multiplatform experience projects whose top level is comprised of people, platforms and content. People include characters, actors, audience, distributors and creators. Platforms include television, Facebook and other relevant social networks, literature, cinema and other media that might be included in the multiplatform experience. Content refers to discreet media texts employed within the platform, such as tweet, a You Tube video, a Facebook post, an email, a television episode, etc. Core content is produced by the creators’ multiplatform experiences to advance the narrative, while complimentary content generated by audience members offers further contributions to the experience. Equally important is the timing with which the components of the experience are introduced and how they interact with and impact upon each other. Being able to combine, filter and sort these elements in multiple ways we can better understand the value of certain components of a project. It also offers insights into the relationship between the timing of the release of components and user activity associated with them, which further highlights the efficacy (or, indeed, failure) of assets as catalysts for engagement. In collaboration with Hoodlum we have developed a number of design scenarios experimenting with the ways in which data can be visualised and manipulated to tell a more refined story about the value of user engagement with certain project components and activities. This experimentation will serve as the basis for future research.

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Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

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INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2013 evaluation campaign, which consisted of four activities addressing three themes: searching professional and user generated data (Social Book Search track); searching structured or semantic data (Linked Data track); and focused retrieval (Snippet Retrieval and Tweet Contextualization tracks). INEX 2013 was an exciting year for INEX in which we consolidated the collaboration with (other activities in) CLEF and for the second time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums. This paper gives an overview of all the INEX 2013 tracks, their aims and task, the built test-collections, and gives an initial analysis of the results