A concept for adapting medical education to the next generations via three-staged digital peer teaching key feature cases.

While the core principles of medical education remain the same, the students' socioecological backgrounds, values and learning requirements are constantly changing. Bridging the generation gap between teachers and students is a key challenge of medical didactics. To meet the demands of today's classroom, we piloted a novel three-stage peer teaching and key feature concept. First, an on-demand key feature video case was presented. Second a background video was launched, followed by a self-assessment tool. Third, a live case discussion webinar focusing on clinical reasoning was held. The contents were created by near-peers experienced in medical didactics and checked by clinical experts. The elective format resonated with 652 participating graduate students and 1250 interactions per webinar, suggesting that students' strengths and weaknesses were addressed adequately. We aim to provide educators with input for creating a flexible and integrative learning environment utilising modern technological and didactic tools that shape the healthcare workers of tomorrow.

Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.

Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives.Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%.Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity.Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

Modeling air-to-air communication networks in the North Atlantic region.

The North Atlantic is one of the world's airspaces accommodating a very high aircraft density while at the same time no radio coverage or radar surveillance is available. Beside satellite communication, one approach to enable data communication between aircraft and ground in the North Atlantic region is to establish ad-hoc  networks build up by direct data links between the aircraft that are acting as communication nodes. In this paper we, therefore, present a modeling approach to model air traffic and ad-hoc networks in the North Atlantic region using up-to-date flight plans and trajectory modeling techniques and to assess the connectivity provided by such networks. Assuming an applicable set of ground stations that provide data transfer to and from this airborne network, we assess the connectivity by time-series analysis and in total for a set of different fractions of all aircraft assumed to be equipped with the necessary systems as well as for a variation of the air-to-air communication range. In addition, we present average link durations, average amounts of hops to reach ground and numbers of connected aircraft for the different scenarios and identify general relations between the different factors and metrics. We will show, that communication range and equipage fraction significantly influence the connectivity of such networks.

An explanatory approach to modeling the fleet assignment in the global air transportation system.

Airlines' fleet assignment heavily affects the economic and ecological performance of the global air transportation system (ATS). Consequently, it is inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios. Therefore, this article presents a novel explanatory approach to modeling the fleet assignment in the global ATS. The presented approach is based on formulating and solving an optimization problem, which describes the fleet assignment in the ATS through a suitable combination of objective function and constraints. While the objective function combines both the airline and the passenger perspective on the fleet assignment, the constraints include additional operational and technological aspects. In comparison to the available global fleet assignment models in the literature, which rely on statistical approaches, the advantages of the presented approach via an optimization problem lie in the overall scenario capability and the consideration of explicit aircraft types instead of simplifying seat categories. To calibrate and validate our model, we use 10 years of historic flight schedule data. The results underline the strengths and weaknesses of the presented approach and indicate potential for future improvement.

Spectral Fusion of Heartbeat and Accelerometer Data for Estimation of Breathing Rate in Wearable Patches.

Despite developments in wearable devices for detecting various bio-signals, continuous measurement of breathing rate (BR) remains a challenge. This work presents an early proof of concept that employs a wearable patch to estimate BR. We propose combining techniques for calculating BR from electrocardiogram (ECG) and accelerometer (ACC) signals, while applying decision rules based on signal-to-noise (SNR) to fuse the estimates for improved accuracy.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons.

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

Evaluating the climate impact of aviation emission scenarios towards the Paris agreement including COVID-19 effects.

Aviation is an important contributor to the global economy, satisfying society's mobility needs. It contributes to climate change through CO2 and non-CO2 effects, including contrail-cirrus and ozone formation. There is currently significant interest in policies, regulations and research aiming to reduce aviation's climate impact. Here we model the effect of these measures on global warming and perform a bottom-up analysis of potential technical improvements, challenging the assumptions of the targets for the sector with a number of scenarios up to 2100. We show that although the emissions targets for aviation are in line with the overall goals of the Paris Agreement, there is a high likelihood that the climate impact of aviation will not meet these goals. Our assessment includes feasible technological advancements and the availability of sustainable aviation fuels. This conclusion is robust for several COVID-19 recovery scenarios, including changes in travel behaviour.