Infants’ remarkable learning abilities allow them to rapidly acquire many complex skills. It has been suggested that infants achieve this learning by optimally allocating their attention to relevant stimuli in the environment, but the underlying mechanisms remain poorly understood. Here, we modeled infants’ looking behavior during a learning task through an ideal learner that quantified the informational structure of environmental stimuli. We show that saccadic latencies, looking time, and time spent engaged with a stimulus sequence are explained by the properties of the learning environments, including the level of surprise of the stimulus, overall predictability of the environment, and progress in learning the environmental structure. These findings reveal the factors that shape infants’ advanced learning, emphasizing their predisposition to seek out stimuli that maximize learning.

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Author ContributionsMM, JEvS and SH jointly developed the study concept and design. MM and JEvS collected the data. MM and JEvS performed EEG data analyses, and MM and FP performed computational modelling analyses.

In modern turbomachinery design, one of the main objectives of aviation industry is the continuous research for higher performance with lighter engines. This trend leads to a reduction in the number of blades, which become increasingly thin and loaded, with a consequent increase in the occurrence of aeroelastic phenomena, compromising the structural integrity. This paper aims to present a numerical flutter assessment of two different types of blade assembly: a turbine cluster system typical of stator segments and an intentionally mistuned row representing an up-to-date low pressure turbine rotor. The numerical results obtained by a time-accurate CFD solver with vibrating blades will be compared with experimental data measured in the context of the EU project FUTURE. The first part of the paper will describe the study of a stator turbine cascade assembly, whose blades are mounted in packets and vibrate as a cluster mode. The comparison between numerical and experimental data showed an excellent agreement and further validated the aeroelastic solver. Then, the attention will be focused on the flutter analysis of an intentionally mistuned turbine rotor bladerow in comparison with a traditional row consisting of identical blades: this highlights how this type of assembly may stabilize the bladerow. The results of the numerical blade stability analysis show a flutter instability for the first bending mode which becomes stable, once the modal mistuning is introduced by adding masses at the tip of alternate blades. This numerically predicted flutter stabilization was confirmed by the experimental campaigns.

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