Abstract: An air conditioning device for a vehicle includes: a housing having an inside divided into an inflow space, a heat exchange space, and an outflow space, which are straightly arranged, and having a plurality of discharge ports, which communicates with an interior, at the inflow space; a blowing unit disposed at the inflow space of the housing and configured to blow air; a heat exchange unit disposed at the heat exchange space of the housing and configured to adjust a temperature of conditioned air by exchanging heat with air; and an opening-closing door disposed at the outflow space of the housing and configured to open and close the plurality of discharge ports such that conditioned air at an adjusted temperature selectively flows to the plurality of discharge ports. The air conditioning device adjusts the temperature of conditioned air for respective modes and reduces a flow resistance of air.

Abstract: Proposed is a CO2-reduction membrane electrode assembly (MEA), which is a novel MEA capable of changing a reaction condition to an alkaline condition from a problematic acidic condition unfavorable to reactions on a cathode side during catalytic reactions using a cation exchange membrane (CEM) as a separator. In addition, the MEA can reduce a hydrogen evolution reaction (HER), which is a side reaction. In addition, a method of manufacturing the MEA, and a CO2-reduction assembly including the MEA are also proposed.

Abstract: Techniques are provided for using machine learning methods to predict user preferences with respect to robotic assistive prostheses and/or custom tune the robotic assistive prostheses for individual users based on the predicted user preferences. A training biomechanical dataset, including historical biomechanical sensor data for a robotic assistive prosthetic device operating using a plurality of tuning settings at a plurality of speeds, and a training user preference dataset including historical user tuning preference data for the robotic assistive prosthetic device for each respective tuning setting and speed, are generated. A machine learning model is trained using the training biomechanical dataset and the training user preference dataset.