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Two series of composite micro/mesoporous materials with different contributions of micro-and mesoporosity were prepared by dealumination and recrystallization of mordenite zeolite. The materials were characterized by X-ray diffraction, infrared spectroscopy, 27 Al magic angle spinning (MAS) NMR, nitrogen adsorption-desorption, and temperatureprogrammed desorption of ammonia (TPD NH 3 ). Catalytic properties were studied in transalkylation of biphenyl with diisopropylbenzene. Both types of composite materials showed remarkably high activity, stability, and selectivity toward formation of diisopropylbiphenyls with respect to both pure microporous and mesoporous materials. The effect is due to high zeolitic acidity combined with improved accessibility of active sites and transport of bulky molecules provided by mesopores.

Mutual adaptation is critical for effective team collaboration. This paper presents a formalism for human-robot mutual adaptation in collaborative tasks. We propose the bounded-memory adaptation model (BAM), which captures human adaptive behaviors based on a bounded memory assumption. We integrate BAM into a partially observable stochastic model, which enables robot adaptation to the human. When the human is adaptive, the robot will guide the human towards a new, optimal collaborative strategy unknown to the human in advance. When the human is not willing to change their strategy, the robot adapts to the human in order to retain human trust. Human subject experiments indicate that the proposed formalism can significantly improve the effectiveness of human-robot teams, while human subject ratings on the robot performance and trust are comparable to those achieved by cross training, a state-of-the-art human-robot team training practice.

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