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Previous studies provided evidence of the claim that the prediction of occluded action involves real-time simulation. We report two experiments that aimed to study how real-time simulation is affected by simultaneous action execution under conditions of full, partial or no overlap between observed and executed actions. This overlap was analysed by comparing the body sides and the movement kinematics involved in the observed and the executed action. While performing actions, participants observed point-light (PL) actions that were interrupted by an occluder, followed by a test pose. The task was to judge whether the test pose depicted a continuation of the occluded action in the same depth angle. Using a paradigm proposed by Graf et al., we independently manipulated the duration of the occluder and the temporal advance of the test pose relative to occlusion onset (occluder time and pose time, respectively). This paradigm allows the assessment of real-time simulation, based on prediction performance across different occluder time/pose time combinations (i.e., improved task performance with decreasing time distance between occluder time and pose time is taken to reflect real-time simulation). The PL actor could be perceived as from the front or back, as indicated by task instructions. In Experiment 1 (front view instructions), evidence of action simulation was obtained for partial overlap (i.e., observed and performed action corresponded either in body side or movement kinematics), but not for full or no overlap conditions. The same pattern was obtained in Experiment 2 (back view instructions), ruling out a spatial compatibility explanation for the real-time pattern observed. Our results suggest that motor processes affect action prediction and real-time simulation. The strength of their impact varies as a function of the overlap between observed and executed actions.
Advanced mechatronic systems have to integrate existing technologies from mechanical, electrical and software engineering. They must be able to adapt their structure and behavior at runtime by reconfiguration to react flexibly to changes in the environment. Therefore, a tight integration of structural and behavioral models of the different domains is required. This integration results in complex reconfigurable hybrid systems, the execution logic of which cannot be addressed directly with existing standard modeling, simulation, and code-generation techniques. We present in this paper how our component-based approach for reconfigurable mechatronic systems, MECHATRONIC UML, efficiently handles the complex interplay of discrete behavior and continuous behavior in a modular manner. In addition, its extension to even more flexible reconfiguration cases is presented.
Advanced mechatronic systems have to integrate existing technologies from mechanical, electrical and software engineering. They must be able to adapt their structure and behavior at runtime by reconfiguration to react flexibly to changes in the environment. Therefore, a tight integration of structural and behavioral models of the different domains is required. This integration results in complex reconfigurable hybrid systems, the execution logic of which cannot be addressed directly with existing standard modeling, simulation, and code-generation techniques. We present in this paper how our component-based approach for reconfigurable mechatronic systems, M ECHATRONIC UML, efficiently handles the complex interplay of discrete behavior and continuous behavior in a modular manner. In addition, its extension to even more flexible reconfiguration cases is presented.
CovRadar
(2022)
The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast.