Abstract
We present a novel fully distributed strategy for joint scheduling of consumption and trading within transactive energy networks. The aim is maximizing social welfare, which itself is redefined and adapted for peer-to-peer prosumer-based markets. In the proposed scheme, hourly energy values are calculated to coordinate the joint scheduling of consumption and trading, taking into consideration both preferences and needs of all network participants. Electricity market prices are scaled locally based on hourly energy values of each prosumer. This creates a system where energy consumption and trading are coordinated based on the value of energy use throughout the day, rather than only the market price. For each prosumer, scheduling is done by allocating load (consumption) and supply (trading) blocks, maximizing the energy value globally and locally within the network. The proposed strategy was tested using a case study of typical residential prosumers. It was shown that the proposed model could provide potential benefits for both prosumers and the grid, albeit with a user-centered, fully distributed management model which relies solely on local scheduling in transactive energy networks. © 2019 IEEE.

Abstract
Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50% and 60%) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: (i) model-free and data-driven predictive control; (ii) control philosophy focused on operating the tank with a variable wastewater set-point level; (iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system. The results for a real case-study during 90 days show a 16.7% decrease in electrical energy consumption while still achieving a 97% reduction in the number of alarms (tank level above 7.2 m) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.

Abstract
In an avoidable harmful situation, autonomous vehicles systems are expected to choose the course of action that causes the less damage to everybody. However, this behavioral protocol implies some predictability. In this context, we show that if the autonomous vehicle decision process is perfectly known then malicious, opportunistic, terrorist, criminal and non-civic individuals may have incentives to manipulate it. Consequently, some levels of uncertainty are necessary for the system to be manipulation proof. Uncertainty removes the mis-behavior incentives because it increases the risk and likelihood of unsuccessful manipulation. However, uncertainty may also decrease the quality of the decision process with negative impact in terms of efficiency and welfare for the society. We also discuss other possible solutions to this problem.

Abstract
The use of sensing devices to perform monitoring tasks has continuously evolved in the past decades [...]

Abstract
The development and study of new, more robust and powerful wavefront sensors plays an important role in the improvement of the wavefront sensing capabilities of adaptive optics systems. The LAM-ONERA On-sky Pyramid Sensor is a R&D bench dedicated to study and characterize these new wavefront sensors. In this paper, we give a glance at the current status of the bench in terms of hardware and at the most recent results obtained using new flavours of Fourier filtering wavefront sensors.

Abstract
We propose adding an irregular quadrature amplitude modulation (QAM) constellation to a wireless transmission scheme in order to obtain greater control over the signal-to-noise ratio (SNR) required to successfully decode the signal. By altering the separation between adjacent symbols, the minimum required SNR is raised without degradation in the performance of the scheme. This allows the system to adapt to preferable channel conditions for the authorized user, making it harder for eavesdroppers to intercept and decode the transmission, thus making the communication safer. In addition, we show that by overlaying a coset code onto the QAM constellation, a new, stronger security gap metric can be further improved. Results show the effectiveness of this strategy with an interleaved coding for secrecy with a hidden key (ICSHK) scheme.