Native speakers of English, Korean, and Malayalam (N = 30 in each group) rated their emotional reactions to stories describing events leading to anger, fear, sadness, and disgust. Speaker's language had no significant effect for anger, fear, and sadness stories, but did for disgust stories. The category named by the English word disgust includes emotional reactions to distaste, pathogen-containing substances, blood, sex, and moral violations. The category named by disgust's translations into Korean and Malayalam were narrower, a result that challenges translation equivalence. Lack of equivalence across languages is consistent with the argument that the English word disgust refers to a heterogeneous mix of similar but different emotional reactions.Disgust has been said to be a universal human emotion, and thus the study of disgust requires translation of the word disgust into other languages. Here, we question the claim that the word disgust can be adequately translated. A lack of translation equivalents, in turn, raises a larger question about the nature of the category of emotional reactions denoted by disgust: Is that category homogeneous or heterogeneous? Could the word refer to more than one type of emotion?

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

Prior research on cross-cultural negotiation has emphasized the cognitive and the behavioral elements. This study takes a different perspective and presents a motivation–emotion model of cross-cultural negotiation. We propose that the cultural differences in chronic regulatory focus will lead to cultural biases in emotion recognition, which in turn will affect negotiation behaviors. People are inclined to perceive and behave in ways that enhance regulatory fit. Westerners and East Asians, who each have different chronic regulatory focus, are likely to interpret the negotiation situation differently in order to increase their regulatory fit. Specifically, this study proposes that when the emotion of the opponent is ambiguous, people from different cultural backgrounds may show cultural biases in emotion recognition, concentrating on the emotion that fits their chronic regulatory focus. Drawing on the Emotion as Social Information (EASI) model, this study discusses how these cultural biases in emotion recognition can affect people’s negotiation behaviors. Finally, some possible moderators of the motivation–emotion model including power and emotion recognition accuracy are suggested to promote sustainable practices in cross-cultural negotiation.

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