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This paper presents the status regarding electric vehicles (EVs) and public charging stations managed by Cluj-Napoca City Hall. The types of charging stations and the quantity of the used energy were stated. The behavior of EV owners in charging their vehicles was also studied. The presented work proposes an extension of the EV charging infrastructure to support current and future EV owners. Simulations were carried out in Simulink to create an overview of the existing infrastructure. The simulation results showed the amount of greenhouse gas (CO2 emissions) reduced by using EVs. An estimated number of kilometers traveled with EVs was calculated by tracking the loads in all public charging stations.

The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorithm and analyzed its performance in terms of execution time and quality of the approximation. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the algorithm can provide competitive results for single document summarization and multi-document query-based summarization. On the Cornell Newsroom Summarization Dataset, it ranked second among the unsupervised methods tested. For the more challenging task of multi-document query-based summarization, the method was tested on the DUC 2005 Dataset. Our algorithm surpassed the other reported methods with respect to the ROUGE-SU4 metric, and it was at less than 0.01 from the top performing algorithms with respect to ROUGE-1 and ROUGE-2 metrics.