Clubs’ environmentally responsible behavior: The perspectives of club managers in North America

The engagement of corporate environmental responsibility (CER) and the implementation of environmentally responsible behavior (ERB) are crucial for clubs to reduce negative effects on the environment and to build good relationships with stakeholders. However, little research has examined ERB implementation and barriers to ERB by clubs. This study surveyed 3,250 club managers in North America and measured the following three variables: perceived importance of CER by clubs, current ERB practices, and perceived barriers to ERB. Independent samples t-tests and chi-square tests were utilized to compare the differences on these three variables between clubs with sustainable practices (SUS clubs) and those clubs with no sustainable practices (non-SUS clubs). The results of this study showed that SUS clubs considered CER to be more important than non-SUS clubs did. Furthermore, SUS clubs engaged in a greater number of ERB practices and perceived fewer barriers to ERB implementation than non-SUS clubs did. The findings of this study could fill the literature gap that lacks research about perceptions of club managers on ERB of clubs. The findings of this study will also help club managers to improve environmental performance by implementing ERB and overcoming barriers to ERB

DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method

The Mechanical Behavior of the Cable-in-Conduit Conductor in the ITER Project

Cable-in-conduit conductor (CICC) has wide applications, and this structure is often served to undergo heat force-electromagnetic coupled field in practical utilization, especially in the magnetic confinement fusion (e.g., Tokamak). The mechanical behavior in CICC is of relevance to understanding the mechanical response and cannot be ignored for assessing the safety of these superconducting structures. In this chapter, several mechanical models were established to analyze the mechanical behavior of the CICC in Tokamak device, and the key mechanical problems such as the equivalent mechanical parameters of the superconducting cable, the untwisting behavior in the process of insertion, the buckling behavior of the superconducting wire under the action of the thermo-electromagnetic static load, and the Tcs (current sharing temperature) degradation under the thermo-electromagnetic cyclic loads are studied. Finally, we summarize the existing problems and the future research points on the basis of the previous research results, which will help the related researchers to figure out the mechanical behavior of CICC more easily

Hamiltonian Paths in Non-Hamiltonian Graphs

A graph $G$ with $n$ vertices is \emph{Hamiltonian} if it admits an embedded cycle containing all vertices of $G$. In any Hamiltonian graph, each vertex is the starting point of a Hamiltonian path. In this paper we explore the converse. We show that for $2<n<9$, if $G$ admits Hamiltonian paths starting at every vertex then $G$ is Hamiltonian. We also show that this is not true for $n>9$. We then investigate the number of \emph{pairs} of vertices in a non-Hamiltonian graph $G$ which can be connected by Hamiltonian paths. In particular we construct a family of non-Hamiltonian graphs with approximately 4/5 of the pairs of vertices connected by Hamiltonian paths.Comment: 16 pages, 7 figure

NMS Strikes Back

Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture. Code is available at https://github.com/jozhang97/DETA.Comment: Code is available at https://github.com/jozhang97/DET

A competitive mechanism based multi-objective particle swarm optimizer with fast convergence

In the past two decades, multi-objective optimization has attracted increasing interests in the evolutionary computation community, and a variety of multi-objective optimization algorithms have been proposed on the basis of different population based meta-heuristics, where the family of multi-objective particle swarm optimization is among the most representative ones. While the performance of most existing multi-objective particle swarm optimization algorithms largely depends on the global or personal best particles stored in an external archive, in this paper, we propose a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation. The performance of the proposed competitive multi-objective particle swarm optimizer is verified by benchmark comparisons with several state-of-the-art multiobjective optimizers, including three multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed