| 1. |
- Liang, Xiubo, et al.
(författare)
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Menstrual Monster : A Tangible Interactive Co-educational Game Designed for Teenagers
- 2022
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Ingår i: CHI EA '22. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450391566
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Konferensbidrag (refereegranskat)abstract
- Learning menstruation in early adolescence could reduce teenagers’ misunderstanding of it and help them treat menstruation in a proper way. This paper explored a tangible game for teenagers of different genders learning menstruation through collaborative playing. The game included five levels where users play together and learn the cause, products, symptoms of menstruation as well as try to judge some scenarios and listen to audios about menstruation. In our user study, we invited three groups of teenagers ages 11 to 16. Each group contained at least one male and one female, and we let them play the game freely. Teenagers were successfully able to play the game collaboratively, learn menstruation-related knowledge. The results revealed motivation differences related to gender, and after the game, teenagers demonstrated the observable change of the attitude towards menstruation.
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| 2. |
- Xu, Wenjie, et al.
(författare)
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MathKingdom : Teaching Children Mathematical Language Through Speaking at Home via a Voice-Guided Game
- 2023
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Ingår i: CHI '23. - : Association for Computing Machinery (ACM). - 9781450394215
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Konferensbidrag (refereegranskat)abstract
- The amount and quality of mathematical language in the family are positively associated with promoting children’s mathematical abilities. However, mathematical language in many families is poor. Through need-finding investigation, we developed MathKingdom, a voice-agent-based game that helps children aged 4–7 learn and use rich, accurate mathematical language (e.g., mathematical expressions related to measurement, sequence, patterns). The game has four flows, in which users can wake up, transform, decorate, and perform as their avatars, as well as practice basic mathematical vocabulary, mathematical single sentences, coherent mathematical statements, and free expression. We refined the system design through wizard-of-oz testing and then evaluated it with 18 families. The results showed that MathKingdom effectively engaged children, enhanced their mathematical language skills and mathematical abilities, and encouraged parent-child conversations about math.
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| 3. |
- Kang, Ying, et al.
(författare)
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Weak Bonds Joint Effects Catalyze the Cleavage of Strong C−C Bond of Lignin‐Inspired Compounds and Lignin in Air by Ionic Liquids
- 2020
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Ingår i: ChemSusChem. - : John Wiley & Sons. - 1864-5631 .- 1864-564X. ; 13:22, s. 5945-5953
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Tidskriftsartikel (refereegranskat)abstract
- Oxidation of lignin to value‐added aromatics through selective C−C bond cleavage via metal‐free and mild strategies is promising but challenging. It was discovered that the cations of ionic liquids (ILs) could effectively catalyze this kind of strong bond cleavage by forming multiple weak hydrogen bonds, enabling the reaction conducted in air at temperature lower than 373 K without metal‐containing catalysts. The cation [CPMim]+ (1‐propylronitrile‐3‐methylimidazolium) afforded the highest efficiency in C−C bond cleavage, in which high yields (>90 %) of oxidative products were achieved. [CPMim]+ could form three ipsilateral hydrogen bonds with the oxygen atom of C=O and ether bonds at both sides of the C−C bond. The weak bonds joint effects could promote adjacent C−H bond cleave to form free radicals and thereby catalyze the fragmentation of the strong C−C. This work opens up an eco‐friendly and energy‐efficient route for direct valorization of lignin by enhancing IL properties via tuning the cation.
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| 4. |
- Li, Jiayu, et al.
(författare)
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Charge transport and electron-hole asymmetry in low-mobility graphene/hexagonal boron nitride heterostructures
- 2018
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Ingår i: Journal of Applied Physics. - : AIP Publishing. - 0021-8979 .- 1089-7550. ; 123:6
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Tidskriftsartikel (refereegranskat)abstract
- Graphene/hexagonal boron nitride (G/h-BN) heterostructures offer an excellent platform for developing nanoelectronic devices and for exploring correlated states in graphene under modulation by a periodic superlattice potential. Here, we report on transport measurements of nearly 0°-twisted G/h-BN heterostructures. The heterostructures investigated are prepared by dry transfer and thermally annealing processes and are in the low mobility regime (approximately 3000 cm2 V-1s-1 at 1.9 K). The replica Dirac spectra and Hofstadter butterfly spectra are observed on the hole transport side, but not on the electron transport side, of the heterostructures. We associate the observed electron-hole asymmetry with the presence of a large difference between the opened gaps in the conduction and valence bands and a strong enhancement in the interband contribution to the conductivity on the electron transport side in the low-mobility G/h-BN heterostructures. We also show that the gaps opened at the central Dirac point and the hole-branch secondary Dirac point are large, suggesting the presence of strong graphene-substrate interaction and electron-electron interaction in our G/h-BN heterostructures. Our results provide additional helpful insight into the transport mechanism in G/h-BN heterostructures.
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| 5. |
- Wang, Pin, et al.
(författare)
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Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
- 2021
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2021-May, s. 1036-1042
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Konferensbidrag (refereegranskat)abstract
- Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversarial Inverse Reinforcement Learning (AIRL) is one of the state-of-art imitation learning methods that can learn both a behavioral policy and a reward function simultaneously, yet it is only demonstrated in simple and static environments where no interactions are introduced. In this paper, we improve and stabilize AIRL's performance by augmenting it with semantic rewards in the learning framework. Additionally, we adapt the augmented AIRL to a more practical and challenging decision-making task in a highly interactive environment in autonomous driving. The proposed method is compared with four baselines and evaluated by four performance metrics. Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.
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