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

Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection

Graph-level anomaly detection has gained significant attention as it finds applications in various domains, such as cancer diagnosis and enzyme prediction. However, existing methods fail to capture the spectral properties of graph anomalies, resulting in unexplainable framework design and unsatisfying performance. In this paper, we re-investigate the spectral differences between anomalous and normal graphs. Our main observation shows a significant disparity in the accumulated spectral energy between these two classes. Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs. Motivated by this, we propose Rayleigh Quotient Graph Neural Network (RQGNN), the first spectral GNN that explores the inherent spectral features of anomalous graphs for graph-level anomaly detection. Specifically, we introduce a novel framework with two components: the Rayleigh Quotient learning component (RQL) and Chebyshev Wavelet GNN with RQ-pooling (CWGNN-RQ). RQL explicitly captures the Rayleigh Quotient of graphs and CWGNN-RQ implicitly explores the spectral space of graphs. Extensive experiments on 10 real-world datasets show that RQGNN outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in AUC, demonstrating the effectiveness of our framework. Our code is available at https://github.com/xydong127/RQGNN

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

Soil Aggregate Response to Three Freeze-Thaw Methods in a Northeastern China Mollisol

Freeze-thaw (FT) cycles occur annually in soils of mesic and frigid temperature regimes. FT has profound impacts on soil aggregates yet is often difficult to document in field settings. As a result, laboratory-based FT experiments are widely used, albeit with their own limitations. Both laboratory and field-based research indicates that aggregate properties vary with rates of freezing and thawing as well as the number and amplitudes of FT cycles. In this study, we introduce a continuous freezing-to-thawing-to-freezing technique (i.e., “VTR”) and compare it to a commonly used discrete freeze-then-thaw-then-freeze method (i.e., “RTCR”) and compare both results to natural seasonal changes. Our study soil is the A horizon of the major cropped mollisol in northeastern China. We examined it under natural field soil moisture conditions as well as two controlled soil moisture contents in the laboratory. Both RTCR and VTR show a decrease in large (\u3e1mm) aggregate content and a corresponding increase in medium (0.5 to 0.2 mm) aggregates (P\u3e0.05) that is proportional to the number of FT cycles and soil moisture content. Wet aggregate stability (WAS) increased (P\u3c0.05) over the time of the experiment with each method. RTCR data showed an interaction between FT cycles and soil water content. VTR was better, although certainly not with better matched field results than RTCR, which we attribute its FT cycles being matched to anactual field. These results confirm the dependability and authenticity of the VTR technique

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

ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness

During the past few decades, cognitive diagnostics modeling has attracted increasing attention in computational education communities, which is capable of quantifying the learning status and knowledge mastery levels of students. Indeed, the recent advances in neural networks have greatly enhanced the performance of traditional cognitive diagnosis models through learning the deep representations of students and exercises. Nevertheless, existing approaches often suffer from the issue of overconfidence in predicting students' mastery levels, which is primarily caused by the unavoidable noise and sparsity in realistic student-exercise interaction data, severely hindering the educational application of diagnostic feedback. To address this, in this paper, we propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback and is flexible for different cognitive diagnostic functions. Specifically, we first propose a Bayesian method to explicitly estimate the state uncertainty of different knowledge concepts for students, which enables the confidence quantification of diagnostic feedback. In particular, to account for potential differences, we suggest modeling individual prior distributions for the latent variables of different ability concepts using a pre-trained model. Additionally, we introduce a logical hypothesis for ranking confidence levels. Along this line, we design a novel calibration loss to optimize the confidence parameters by modeling the process of student performance prediction. Finally, extensive experiments on four real-world datasets clearly demonstrate the effectiveness of our ReliCD framework