Fuzzy target-environment networks and fuzzy-regression approaches

The notion of comfort with respect to rides, such as roller coasters, is typically addressed from the perspective of a physical ride, where the convenience of transportation is redefined to minimize risk and maximize thrill. As a popular form of entertainment, roller coasters sit at the nexus of rides and games, providing a suitable environment to measure both mental and physical experiences of rider comfort. In this paper, the way risk and comfort affect such experiences is investigated, and the connection between play comfort and ride comfort is explored. A roller coaster ride simulation is adopted as the target environment for this research, which combines the feeling of being thrill and comfort simultaneously. At the same time, this paper also expands research on roller coaster rides while bridging the rides and games via the analogy of the law of physics, a concept currently known as motion in mind. This study’s contribution involves a roller coaster ride model, which provides an extended understanding of the relationship between physical performance and the mental experience relative to the concept of motion in mind while establishing critical criteria for a comfortable experience of both the ride and play.

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Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Sensors ◽

10.3390/s21061960 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1960

Author(s):

Azade Fotouhi ◽

Ming Ding ◽

Mahbub Hassan

Keyword(s):

Degrees Of Freedom ◽

Network Performance ◽

Learning Model ◽

Base Stations ◽

Communication Model ◽

Complex Environments ◽

End User ◽

Q Learning ◽

Trajectory Simulation ◽

Target Environment

In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

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THE LONG-TERM IMPACT OF SOCIAL MOVEMENTS AND REPRESSION ON DEMOCRATIC ATTITUDES

Journal of East Asian Studies ◽

10.1017/jea.2020.16 ◽

2020 ◽

Vol 20 (3) ◽

pp. 317-351

Author(s):

Scott Desposato ◽

Gang Wang

Keyword(s):

Social Movements ◽

High School Students ◽

Fuzzy Regression ◽

School Students ◽

Attitudinal Differences ◽

College Entry ◽

Democratic Attitudes ◽

Long Term Impact ◽

The Impact

AbstractDemocracy movements in authoritarian regimes usually fail and are repressed, but they may still affect attitudes and norms of participants and bystanders. We exploit several features of a student movement to test for enduring effects of social movements on democratic attitudes. College students were the core of the movement and had wide exposure to the ideas and activities of the movement, as well as the suppression of the movement. College-bound high school students had limited exposure to the movement and its activities. Time of college entry could in theory be manipulated and endogenous, so we also use birthdate as an exogenous instrument for enrollment year. Applying a fuzzy regression discontinuity, we test for the impact of exposure to the movement on long-term attitudes. We find significant attitudinal differences between those in college during the movement, and those who started college post-movement. These results are strongest for alumni of the four universities that were most connected to the movement.

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