A modified genetic algorithm (MGA) optimization procedure, alongside time series machine learning (ML) classifiers, is proposed to minimize handovers in a digital twin-based visible light communication (VLC) system. Frequent handovers have a direct impact on the overall performance of the VLC system due to the inherent connection downtime of a handover process. The handover scheme proposed in this article considers the receiver trajectory information to minimize handovers, maintaining the system performance below the forward error correction limit. Simulation results indicate that the proposed scheme outperforms a power-based handover scheme, achieving handover reductions of 42.47%. Therefore, the MGA combined to the ML models approach is an effective means of minimizing handovers, as well as improving overall VLC system performance.
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To prepare medical students appropriately for the management of toxicological emergencies, we have developed a simulation-based medical education (SBME) training in acute clinical toxicology. Our aim is to report on the feasibility, evaluation and lessons learned of this training. Since 2019, each year approximately 180 fifth-year medical students are invited to participate in the SBME training. The training consists of an interactive lecture and two SBME stations. For each station, a team of students had to perform the primary assessment and management of an intoxicated patient. After the training, the students completed a questionnaire about their experiences and confidence in clinical toxicology. Overall, the vast majority of students agreed that the training provided a fun, interactive and stimulating way to teach about clinical toxicology. Additionally, they felt more confident regarding their skills in this area. Our pilot study shows that SBME training was well-evaluated and feasible over a longer period.
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Introduction: Sensor-feedback systems can be used to support people after stroke during independent practice of gait. The main aim of the study was to describe the user-centred approach to (re)design the user interface of the sensor feedback system “Stappy” for people after stroke, and share the deliverables and key observations from this process. Methods: The user-centred approach was structured around four phases (the discovery, definition, development and delivery phase) which were fundamental to the design process. Fifteen participants with cognitive and/or physical limitations participated (10 women, 2/3 older than 65). Prototypes were evaluated in multiple test rounds, consisting of 2–7 individual test sessions. Results: Seven deliverables were created: a list of design requirements, a personae, a user flow, a low-, medium- and high-fidelity prototype and the character “Stappy”. The first six deliverables were necessary tools to design the user interface, whereas the character was a solution resulting from this design process. Key observations related to “readability and contrast of visual information”, “understanding and remembering information”, “physical limitations” were confirmed by and “empathy” was additionally derived from the design process. Conclusions: The study offers a structured methodology resulting in deliverables and key observations, which can be used to (re)design meaningful user interfaces for people after stroke. Additionally, the study provides a technique that may promote “empathy” through the creation of the character Stappy. The description may provide guidance for health care professionals, researchers or designers in future user interface design projects in which existing products are redesigned for people after stroke.
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