Allocation of Electric Taxi Charging: Assessing the Layout of Charging Stations Based on Charging Frequency

Recent decades have witnessed the growth of the electric vehicles (EVs) industry due to technological developments. To overcome emerging environmental issues, some metropolises, i.e., Beijing, have employed electric taxi systems, which require tremendous investments in charging stations. However, the supporting charging facilities for EVs are not complete, and in terms of layout, there is also a situation where some charging stations have long charging queues, but some are unvisited. To overcome these difficulties, this paper aims to establish a set of charging stations layout assessment models for the electric taxi based on charging frequency and put forward targeted policy suggestions to make the charging frequency of each station more balanced, to avoid resource waste and undersupply. In this paper, a mathematical model based on integer programming is established in conjunction with the workflow of the electric taxi; in the case study, simulations are performed using the Anylogic platform and the results are statistically analyzed; moreover, we use real-time data to assess the layout of charging stations near and within the Fourth Ring Road in Beijing. The modeling and simulation results show that there is an imbalance in the current charging stations layout in Beijing. More specifically, there is a problem with charging frequency of some stations, which is being too low and some too high. Also, the charging frequency of stations will vary with passenger distribution factors. We classify the studied charging stations into four categories according to their actual usage characteristics and provide specific analysis and optimization suggestions for the different categories. Based on the assessment system in this paper, we also carried out some policy suggestions for further layout optimization. The optimized layout has a more balanced charging frequency, and the variance of charging frequency is reduced largely

In-situ structural identification of Zr3Al2 type metastable phase during crystallization of a Zr-based MG

A metastable phase was detected using higher energy synchrotron radiation when Zr-based metallic glass (MG) was annealed under vacuum in Linkam hot stage at 848 K. The formation and transformation processes of metastable phase were recorded by synchrotron radiation method. The metastable phase during crystallization was identified as Zr3Al2 structure type according to powder diffraction and TEM analysis. The structure of Zr3Al2 type MCP was experimentally evidenced by 3D diffraction patterns and mathematically described. The identification of Zr3Al2 MCP could be helpful for the understanding of cluster structure of MG

Learning to Branch in Combinatorial Optimization with Graph Pointer Networks

Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and historical features to represent the solver state. The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions. The model is trained to imitate the classic strong branching expert rule by a designed top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. Our approach also outperforms the state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances

Consensusabilization for Continuous-Time High-Order Multiagent Systems with Time-Varying Delays

For the consensus problems of high-order linear multiagent systems with time-varying delays in directed topologies, the LMI based-consensus criterion and NLMI-based consensusabilization (protocol parameters design that makes the multiagent systems achieve consensus) are investigated. Improved Lyapunov-Krasovskii functional is used for establishing the consensus convergence criteria and deriving the corresponding consensus protocol. In order to reduce the conservativeness, some proper free-weighting matrices are added into the derivative of Lyapunov-Krasovskii functional and that only keeps one necessary zoom. The numerical and simulation examples are given to demonstrate the effectiveness of the theoretical results. Compared with existing literatures, the convergence criterion and protocol design proposed have lower conservativeness

ArsR Family Regulator MSMEG_6762 Mediates the Programmed Cell Death by Regulating the Expression of HNH Nuclease in Mycobacteria

Programmed cell death (PCD) is the result of an intracellular program and is accomplished by a regulated process in both prokaryotic and eukaryotic organisms. Here, we report a programed cell death process in Mycobacterium smegmatis, an Actinobacteria species which involves a transcription factor and a DNase of the HNH family. We found that over-expression of an ArsR family member of the transcription factor, MSMEG_6762, leads to cell death. Transcriptome analysis revealed an increase in the genes’ transcripts involved in DNA repair and homologous recombination, and in three members of HNH family DNases. Knockout of one of the DNase genes, MSMEG_1275, alleviated cell death and its over-expression of programmed cell death. Purified MSMEG_1275 cleaved the M. smegmatis DNA at multiple sites. Overall, our results indicate that the MSMEG_6762 affects cell death and is mediated, at least partially, by activation of the HNH nuclease expression under a stress condition

Converging Cooperative Functions into the Nanospace of Covalent Organic Frameworks for Efficient Uranium Extraction from Seawater

Article reports a new strategy for efficient extraction of uranium from seawater via converging the cooperative functions of adsorption–photocatalysis into the nanospace of covalent organic frameworks (COFs). This study establishes multicomponent COFs as promising candidates for efficient uranium extraction from seawater