220,497 research outputs found

    Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications

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    The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame-based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the throughput maximization with a granular spectral efficiency versus SINR function, is formulated and solved. Finally, a multicast-aware user scheduling policy, based on the channel state information, is developed. Thus, substantial multiuser diversity gains are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards.Comment: Accepted for publication to the IEEE Transactions on Wireless Communications, 201

    Analyzing collaborative learning processes automatically

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    In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

    Decision-making in an emergency department: A nursing accountability model

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Introduction Nurses that work in an emergency department regularly care for acute patients in a fast-paced environment, being at risk of suffering high levels of burnout. This situation makes them especially vulnerable to be accountable for decisions they did not have time to consider or have been pressured into. Research objective The objective of this study was to find which factors influence ethical, legal and professional accountability in nursing practice in an emergency department. Research design Data were analysed, codified and triangulated using qualitative ethnographic content analysis. Participants and research context This research is set in a large emergency department in the Midlands area of England. Data was collected from 186 nurses using participant observation, 34 semi-structured interviews with nurses and ethical analysis of 54 applicable clinical policies Ethical considerations Ethical approval was granted by two research ethics committees and the National Health Service Health Research Authority. Results The main result was the clinical nursing accountability cycle model, which showed accountability as a subjective concept that flows between the nurse and the healthcare institution. Moreover, the relations amongst the clinical nursing accountability factors are also analysed to understand which factors affect decision-making. Discussion The retrospective understanding of the factors that regulate nursing accountability is essential to promote that both the nurse and the healthcare institution take responsibility not only for the direct consequences of their actions but also for the indirect consequences derived from previous decisions. Conclusion The decision-making process and the accountability linked to it are affected by several factors that represent the holistic nature of both entities, which are organised and interconnected in a complex grid. This pragmatic interpretation of nursing accountability allows the nurse to comprehend how their decisions are affected, while the healthcare institution could act proactively to avoid any problems before they happen

    Contextual Sensitivity in Grounded Theory: The Role of Pilot Studies

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    Grounded Theory is an established methodological approach for context specific inductive theory building. The grounded nature of the methodology refers to these specific contexts from which emergent propositions are drawn. Thus, any grounded theory study requires not only theoretical sensitivity, but also a good insight on how to design the research in the human activity systems to be studied. The lack of this insight may result in inefficient theoretical sampling or even erroneous purposeful sampling. These problems would not necessarily be critical, as it could be argued that through the elliptical process that characterizes grounded theory, remedial loops would always bring the researcher to the core of the theory. However, these elliptical remedial processes can take very long periods of time and result in catastrophic delays in research projects. As a strategy, this paper discusses, contrasts and compares the use of pilot studies in four different grounded theory projects. Each pilot brought different insights about the context, resulting in changes of focus, guidance to improve data collection instruments and informing theoretical sampling. Additionally, as all four projects were undertaken by researchers with little experience of inductive approaches in general and grounded theory in particular, the pilot studies also served the purpose of training in interviewing, relating to interviewees, memoing, constant comparison and coding. This last outcome of the pilot study was actually not planned initially, but revealed itself to be a crucial success factor in the running of the projects. The paper concludes with a theoretical proposition for the concept of contextual sensitivity and for the inclusion of the pilot study in grounded theory research designs
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