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Recently, a second generation of advanced open government data (OGD) infrastructures has emerged, influenced by the principles of the Web 2.0 paradigm, and oriented towards the elimination of the clear distinction between providers and consumers of such data, and the support of data 'prosumers'. This paper presents and validates a methodology for evaluating these advanced second generation of ODG infrastructures, which is based on the estimation of value models of them from users' ratings. This value model includes assessments of the various types of value generated by such an infrastructure, and also of the relations among them as well. This enables a deeper understanding of the whole value generation mechanism and a rational definition of improvement priorities. The proposed methodology has been used for the evaluation of an advanced second generation ODG e-Infrastructure developed in the European project ENGAGE.

Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). The introduction of Reinforcement Learning (RL) in ATSC as tackled those types of congestions by using on-line learning, which is based on the trial and error approach. Furthermore, RL is prone to the dimensionality curse related to the state–action space size based on which a non-linear quality function is derived. The Deep Reinforcement Learning (DRL) framework uses Deep Neural Networks (DNN) to digest raw traffic data to approximate the quality function of RL. This paper provides a comprehensive analysis of the most recent DRL approaches used for the ATSC algorithm design. Special emphasis is set to overview of the traffic state representation and multi-agent DRL frameworks applied for the large traffic networks. Best practices are provided for choosing the adequate DRL model, hyper-parameters tuning, and model architecture design. Finally, this paper provides a discussion about the importance of the open traffic data concept for the extensive application of DRL in the real world ATSC.

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The production of methyl esters (biodiesel) from free fatty acids (FFAs) contained in vegetable oils was studied using a heterogeneous acid catalyst. The feedstock was a by-product of a vegetable oil refinery. The experiments were performed in a batch reactor, in a temperature range of 363.15-393.15 K, with an initial molar ratio of methanol to FFAs of 6.6/1, while the catalyst mass was fixed at 2 wt % of the total vegetable oil mass. A technical kinetic model has been developed which accounts for the reversible esterification reaction. Kinetic parameters were determined by fitting experimental data to the model.