Fine aerosol and PAH carcinogenicity estimation in outdoor environment of Mumbai City, India

2012 ◽  
Vol 22 (2) ◽  
pp. 134-149 ◽  
Author(s):  
Elizabeth J. Abba ◽  
Seema Unnikrishnan ◽  
Rakesh Kumar ◽  
Balkrishna Yeole ◽  
Zohir Chowdhury
Author(s):  
Malar Chellasivalingam ◽  
Laxmeesha Somappa ◽  
Adam M. Boies ◽  
Maryam Shojaei Baghini ◽  
Ashwin A. Seshia

2021 ◽  
pp. 1420326X2199462
Author(s):  
Stefano Ridolfi ◽  
Susanna Crescenzi ◽  
Fabiana Zeli ◽  
Stefano Perilli ◽  
Stefano Sfarra

This work is centred on an ancient Italian church. Since 2011, a restoration plan has been undertaken by following sequential phases. The methodological approach to restoration was guided by environmental monitoring campaigns. In particular, two thermo-hygrometric campaigns were carried out during the warm months of the years 2015 and 2016. The first set of measurements was executed during the restoration of facades and roofs, making it possible to reach even areas that are usually difficult to access. The second set was performed to evaluate the indoor thermo-hygrometric conditions following the work of the previous year. This was intended to assess their differences in variability, the influence of the outdoor environment and any real and perceived improvement. Results demonstrate that thermal images helped in identifying both the heat sources causing thermal discomforts and the good thermal capacity of masonries. Concerning the heat index (HI), the church showed an improvement in the trend of malaise perceived by people during the second summer period (∼2°C lower than 2015). Finally, in the last microclimate monitoring, the roof structure no longer acted as an amplifier for daily temperature excursions.


2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.


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