A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Purpose: The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes. Design/methodology/approach: The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model. Findings: This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically. Originality/value: The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses

Fighting COVID-19: What’s in a Name?

While the COVID-19 virus has infected over 3 million people in the United States of America, Asian Americans face unique unfair treatment due to COVID-19. In America, many anti-Asian incidents have been reported, and the FBI warns of increased hate crimes to Asian Americans due to COVID-19. Americans and high-level politicians use inappropriate names, such as “Chinese Virus,” for the COVID-19 virus, which fuels racism and xenophobia. In this Experience piece, we discuss the harm of referring to the COVID-19 virus based on the geographic location where it was first identified

Constraints on the Local Cosmic Void from the Pantheon Supernovae Data

In principle, the local cosmic void can be simply modeled by the spherically symmetric Lemaitre-Tolman-Bondi (LTB) metric. In practice, the real local cosmic void is probably not spherically symmetric. In this paper, to reconstruct a more realistic profile of the local cosmic void, we divide it into several segments. Each segment with certain solid angle is modeled by its own LTB metric. Meanwhile, we divide the 1048 type Ia supernovae (SNIa) of the Pantheon Survey into corresponding subsets according to their distribution in the galactic coordinate system. Obviously, each SNIa subset can only be used to reconstruct the profile of one segment. Finally, we can patch together an irregular profile for the local cosmic void with the whole Pantheon sample. Note that, the paucity of each data subset lead us to focus on the inner part of each void segment and assume that the half radii of the void segments are sufficient to constrain the whole segment. We find that, despite $2\sigma$ signals of anisotropy limited to the depth of the void segments, the constraints on every void segment are consistent with $\Lambda$CDM model at $95\%$ CL. Moreover, our constraints are too weak to challenge the cosmic homogeneity and isotropy.Comment: 12 pages, 9 figure

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://github.com/newlei/LRGCCF.Comment: The updated version is publised in AAAI 202

Sc-phthalocyanine sheet: Promising material for hydrogen storage

It has been a long-standing dream to have high surface area materials with isolated and exposed transition-metal ions for hydrogen storage. The flexible synthesis procedure proposed recently by M. Abel, et al. [J. Am. Chem. Soc. 133, 1203 (2011)] and A. Sperl et al. [J. Am. Chem. Soc. 133, 11007 (2011)] provides a different pathway to achieve this goal. Using first-principles theory and grand canonical Monte Carlo simulation, we carry out a systematic study of 3d transition metals (Sc to Zn)-phthalocyanine porous sheets and find that Sc-phthalocyanine can store 4.6 wt. % hydrogen at 298 K and 100 bar