Abstract
Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.Purpose: This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.Methods: Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.Results: Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% -71.11%), logistic regression techniques (F1-score: 39.91% -71.13%), and tree-based machine learning models (F1-score: 45.07% -73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.Conclusions: Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

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Abstract
To ensure the safety of passengers concerning virus propagation, such as COVID-19, and keep the turnaround time at low levels, airlines should seek efficient aircraft boarding strategies in terms of both physical distancing and boarding times. This study seeks to analyze the impact of different boarding strategies in the context of the International Air Transport Association's recommendations during the pandemic to reduce interference and physical contact between passengers in airplanes. Boarding strategies such as back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal have been tested in this context. This study extends the previous literature using discrete event simulation to evaluate the impact of the occupation of the middle seat by family members only. This study also analyses the impact of having passengers carrying hand luggage and priority passengers on the performance of these strategies concerning boarding times. In general, the simulation results revealed a 15% improvement in boarding times when the reverse pyramid strategy is used compared to a random strategy, which essentially results from a reduction in the boarding interferences between passengers. The results also show that Steffen's strategy is the best performing, while the blocks strategy results in the worst performance. This study has practical implications for airline companies concerning both operation efficiency and passenger safety.

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This research focuses on developing a wearable device that aims to enhance problem-solving and communication abilities within the context of Industry 4.0. The wearable is being developed in the Continental Advanced Antenna, and it allows operators to notify material shortages on the manufacturing line and helps minimize workflow disturbance. The wearable gives a list of missing materials using context-aware computing, allowing operators to identify and prioritize the missing item quickly. We used the Quick and Dirty usability testing approach to ensure the device's usability and efficacy, allowing quick feedback and iterative modifications throughout the development process. Experienced consultants of project participated initial tests on the device and found that it has the potential to improve efficiency and communication in an industrial setting. However, further testing involving end users is necessary to optimize the device for the unique demands of the production environment. This paper offers valuable insights into the lessons learned from the project and proposes potential future research directions.

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This article investigates the role of storytelling in video game localisation and its impact on players’ immersion and overall gaming experience. While these topics have been extensively studied and developed within the research community, there is still a lack of information combining them in a practical study specific to a particular genre or video game. Using grounded theory, we conducted a study using The Witcher III: Wild Hunt as a case study (role-playing game). We had 41 participants play the video game in two localised versions (English and Brazilian-Portuguese), complete questionnaires, and be interviewed about their gameplay experience after each version. The results provided design recommendations to enhance video game immersion (language and voice-acting) and highlight certain aspects that game designers should consider to further intensify players’ immersion during gameplay.

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The Mid-infrared ELT Imager and Spectrograph (METIS) is is one of three first-generation science instruments for the Extremely Large Telescope (ELT) and has recently completed its final design phase. Its Single Conjugate Adaptive Optics (SCAO) system will provide the performance of an extreme adaptive optics system which enables high contrast imaging observations in the thermal/mid-infrared wavelength domain (3 µm – 13.3 µm). The Real-Time Computer (RTC) is the central component of the SCAO real-time control system. It executes the time critical wavefront control loop as well as associated control tasks by processing the data from the pyramid wavefront sensor and controlling the set of ELT actuators dedicated to adaptive optics. A total of up to 4,866 commands to be computed at a loop rate of up to 1 kHz imposes a number of demanding constraints in terms of memory throughput and computing power on the Hard Real-Time Core (HRTC), which employs GPU acceleration for the bulk of computations. Several auxiliary functions need to be in place to establish and maintain the quality of the wavefront correction. Among them are the control of the pupil position, the compensation of misregistration and of non-common path aberration, and the adaptation of the temporal control parameters. The main wavefront control loop has been prototyped to verify timing requirements. A median RTC computation time of 382 µs was achieved for a 300k samples (5 minutes) run. The results are presented in this paper together with the foreseen RTC hardware and the software deployment within the SCAO Control System.