The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.

High-speed actuation of soft actuators requires high source pressure to transfer air as quickly as possible into the actuator. Allowing high source pressure and having the deformation angle as the only control input may allow faster actuation, but there is a risk of bursting when the actuator is prevented from reaching the desired angle, for example, due to an obstacle. The other option to control the actuator’s deformation is based on controlling the pressure. In this paper, we present a simple control strategy that uses an initial pressure boost in a pressure-based PID controller showing the same performance as in angle-based PID control. The performance improvement is demonstrated experimentally on a standard soft bending actuator and a gecko-inspired, climbing soft robot.