Long Run Trends in the Price of Energy and Energy Services

4 p.

Local air pollution and global climate change taxes: a distributional analysis

25 p.

Solutions for New Terrestrial Broadcasting Systems Offering Simultaneously Stationary and Mobile Services

221 p.

Air Quality and Europeans' Subjective Well-being

[EN]Happiness economics deals with self-reported subjective well-being, or life satisfaction, and its relationship to a wide variety of other variables. On the study of these other factors, this line of research has helped demonstrate that higher levels of environmental quality increase people’s subjective well-being. This paper focuses on analyzing the relationship between subjective well-being and air quality. On the one hand, the life satisfaction approach to environmental valuation is cautiously described, and on the other hand, the method is implemented in an empirical analysis that seeks to assess how an increase in the level of air pollution at a regional level affects individual-level subjective well-being in Europe. We use a dataset that merges the third wave of the European Social Survey (ESS) with a dataset that includes regional air pollution (including CO, PM10, NO2, SO2 and Benzene) and other regional variables. We find a robust negative impact for CO, a positive impact for SO2, and no conclusive evidence of any effect on subjective well-being for the remaining three pollutants.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

System architecture and deployment scenarios for SESAME: Small cEllS coordinAtion for Multi-tenancy and Edge services

The surge of the Internet traffic with exabytes of data flowing over operators mobile networks has created the need to rethink the paradigms behind the design of the mobile network architecture. The inadequacy of the 4G UMTS Long term Evolution (LTE) and even of its advanced version LTE-A is evident, considering that the traffic will be extremely heterogeneous in the near future and ranging from 4K resolution TV to machine-type communications. To keep up with these changes, academia, industries and EU institutions have now engaged in the quest for new 5G technology. In this paper we present the innovative system design, concepts and visions developed by the 5G PPP H2020 project SESAME (Small cEllS coordinAtion for Multi-tenancy and Edge services). The innovation of SESAME is manifold: i) combine the key 5G small cells with cloud technology, ii) promote and develop the concept of Small Cellsas- a-Service (SCaaS), iii) bring computing and storage power at the mobile network edge through the development of nonx86 ARM technology enabled micro-servers, and iv) address a large number of scenarios and use cases applying mobile edge computing. Topics: