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As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for humanoids, is generally not enough to guarantee acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This paper discusses the connection between comfort and similarity of the robot movement to the human one.

To achieve improved multi-node temperature estimation with limited training data in Permanent Magnet Synchronous Motors (PMSMs), a novel approach of a Lumped-Parameter Thermal Network (LPTN)-informed neural network is proposed in this paper. Firstly, the parameter and model uncertainties of third or higher-order LPTNs with global parameter identification for temperature estimation are systematically stated based on numerical analysis.

Quantification and analysis of global oil trade networks reveals deep insights into a nation's development and influence at a global scale. Further, it allows us to predict future trends and changes to adapt state policy as the crude oil market influences the balance of power among the developed and emerging economies alike as it is central for energy needs as well for industrial progress.

Faces and bodies provide critical cues for social interaction and communication. Their structural encoding depends on configural processing, as suggested by the detrimental effect of stimulus inversion for both faces (i.e., face inversion effect - FIE) and bodies (body inversion effect - BIE). An occipito-temporal negative event-related potential (ERP) component peaking around 170 ms after stimulus onset (N170) is consistently elicited by human faces and bodies and it is affected by the inversion of these stimuli.