16,762 research outputs found

    Data-driven design of intelligent wireless networks: an overview and tutorial

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    Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

    Commercial-off-the-shelf simulation package interoperability: Issues and futures

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    Commercial-Off-The-Shelf Simulation Packages (CSPs) are widely used in industry to simulate discrete-event models. Interoperability of CSPs requires the use of distributed simulation techniques. Literature presents us with many examples of achieving CSP interoperability using bespoke solutions. However, for the wider adoption of CSP-based distributed simulation it is essential that, first and foremost, a standard for CSP interoperability be created, and secondly, these standards are adhered to by the CSP vendors. This advanced tutorial is on an emerging standard relating to CSP interoperability. It gives an overview of this standard and presents case studies that implement some of the proposed standards. Furthermore, interoperability is discussed in relation to large and complex models developed using CSPs that require large amount of computing resources. It is hoped that this tutorial will inform the simulation community of the issues associated with CSP interoperability, the importance of these standards and its future

    Enhancing health care non-technical skills: the TINSELS programme

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    Background and Context: Training in ‘non-technical skills’, social (communication and team work) and cognitive (analytical and personal behaviour) skills, in healthcare have been of great interest over the last decade. Whilst the majority of publications focus on ‘whether’ such education can be successful, they overlook the question of ‘how’ they enhance skills. We designed and piloted an original, theoretically robust and replicable teaching package that addresses non-technical skills in the context of medicines safety through simulation-based inter professional learning: the TINSELS (Training In Non-technical Skills to Enhance Levels of Medicines Safety) Programme. Innovation: A modified Delphi process was completed to identify learning outcomes, and recruitment of multi-professional teams was through local publicity. The faculty developed a three-session simulation based intervention: session one was a simulated ward encounter with multiple medicine related activities; session two was an extended debrief and facilitated discussion; and session three a ‘chamber of horrors’ where inter professional teams identified potential sources of error. Each session was completed in the simulation suite with 6 – 9 participants, lasted approximately 90m minutes, and took place over 2 weeks. Full details of the course will be presented to facilitate dissemination. Implications: Likert scale feedback was collected after the course (1 strongly disagree-5 strongly agree). Mean scores were all greater than 4, with qualitative feedback noting the fidelity of the authentic inter professional learner groups. A previously validated safety attitudes questionnaire found changes in attitudes towards handover of care and perceptions of safety levels in the workplace post intervention. An original, simulation based, multi-professional training programme has been developed with learning and assessment materials available for widespread replication
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