We present a comparative study on a 700-yr sequence of dendrochronologically ordered tree-rings of Pinus cembra originating from Eastern Carpathians for the period AD 1009–1709. This period covers the solar minima of the Little Ice Age. The aim of this study was to assess the accuracy of our radiocarbon (14C) determinations interpreted on the IntCal13 calibration data and to observe any apparent offsets. The 14C measurements on single and double tree-rings were “wiggle-matched” to secure the dendrochronology cross-matching of all the Pinus cembra wood pieces. The results showed a very good agreement between the age datasets for four out of five wood trunks. However, for one of them a new cross-matching was performed after a quality assurance test, establishing an earlier 48-yr position, recommended by wiggle-matching Bayesian statistics and dendrochronological analysis. Following this adjustment, the quantification of the 14C level variability with respect to the IntCal13 calibration curve was obtained by calculating Δ14C for all tree-ring samples. As a final conclusion, an insignificant 14C concentration offset of –0.63 ± 3.76‰ was found for the Romanian samples.

As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

No abstract

No abstract

The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impending this process, ranging from insufficient charging infrastructure, battery capacity, long queueing and charging time, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fueled by the lack the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that comes to bypass the limitations of the Reserve now function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station's reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations.