Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling

It is now a matter of scientific consensus that priming, a recency effect of activation in memory, has a significant impact on language users’ choice of linguistic means. However, it has long remained unclear how priming effects coexist with the creative aspect of language use, and the importance of the latter has been somewhat downplayed. By introducing the results of two experiments, for English and Russian native speakers, this paper seeks to explain the mechanisms establishing balance of priming and language creativity. In study 1, I discuss the notion of collective language creativity that I understand as a product of two major factors interacting: cognitive priming effects and the unsolicited desire of the discourse participants to be linguistically creative, that is, to say what one wants to say using the words that have not yet been used. In study 2, I explore how priming and antipriming effects work together to produce collective language creativity. By means of cluster analysis and Bayesian network modelling, I show that patterns of repetition for both languages differ drastically depending on whether participants of the experiment had to communicate their messages being or not being able to see what others had written before them.

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Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

Entropy ◽

10.3390/e21040379 ◽

2019 ◽

Vol 21 (4) ◽

pp. 379 ◽

Cited By ~ 1

Author(s):

Victor Gomez Comendador ◽

Rosa Arnaldo Valdés ◽

Manuel Villegas Diaz ◽

Eva Puntero Parla ◽

Danlin Zheng

Keyword(s):

Bayesian Network ◽

Traffic Control ◽

Air Traffic ◽

Network Modelling ◽

Traffic Demand ◽

Complexity Metrics ◽

Dynamic Airspace Configuration ◽

Input Variables ◽

The Impact

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

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