Modelling of sediment transport and bed deformation in rivers with continuous bends

Peer reviewedPostprin

Empirical Study of the Effect of Website Information Architecture on Customer Loyalty

Website customer loyalty is believed to be very important to the success of E-Commerce firms. Based on document review, this study constructs a customer loyalty effect model which chooses organization, navigation, labeling and searching system as dependent variables as well as attitude and behavior loyalty as variables. The sample of the study was drawn from 250 students who have online shopping experience, 202 of usable questionnaires were included in the data analysis. Data analyses using correlation and regression analysis reveal that there was a strong and positive relationship between information architecture and customer loyalty. Also, organization, navigation, labeling and search systems were positively correlated with customer loyalty. The results could be a reference for designing of the E-Commerce website

Numerical modeling of flow in continuous bends from Daliushu to Shapotou in Yellow River

YesThe upper reach of the Yellow River from Daliushu to Shapotou consists of five bends and has complex topography. A two-dimensional Re-Normalisation Group (RNG) k-ε model was developed to simulate the flow in the reach. In order to take the circulation currents in the bends into account, the momentum equations were improved by adding an additional source term. Comparison of the numerical simulation with field measurements indicates that the improved two-dimensional depth-averaged RNG k-ε model can improve the accuracy of the numerical simulation. A rapid adaptive algorithm was constructed, which can automatically adjust Manning's roughness coefficient in different parts of the study river reach. As a result, not only can the trial computation time be significantly shortened, but the accuracy of the numerical simulation can also be greatly improved. Comparison of the simulated and measured water surface slopes for four typical cases shows that the longitudinal and transverse slopes of the water surface increase with the average velocity upstream. In addition, comparison was made between the positions of the talweg and the main streamline, which coincide for most of the study river reach. However, deviations between the positions of the talweg and the main streamline were found at the junction of two bends, at the position where the river width suddenly decreases or increases.National Natural Science Foundation of China (Grants No. 11361002 and 91230111), the Natural Science Foundation of Ningxia, China (Grant No. NZ13086), the Project of Beifang University of Nationalities, China (Grant No. 2012XZK05), the Foreign Expert Project of Beifang University of Nationalities, China, and the Visiting Scholar Foundation of State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, China (Grant No. 2013A011)

Geometric and Computational Aspects of Manipulation Rules for Graph-Based Engineering Diagrams

The digitization of graph-based engineering diagrams like P&IDs or circuit drawings from optical sources as well as their subsequent processing involves both image understanding and semantic technologies. More precisely, after a raw graph has been obtained by an object detection and line extraction pipeline, semantic gaps (like resolving material flow directions) need to be overcome to retain a comprehensive, semantically correct graph. Likewise, the graph representation often needs to be altered to achieve interoperability with established CAE systems and to accommodate customer-specific requirements. Semantic technologies provide powerful tools to manipulate such data but usually require rather complicated implementation. Graphically presentable graph based rules provide a code-free mean to ease the interaction with domain experts. In order to be applicable in real-world applications, both geometric and computational aspects need to be considered. This paper explores these aspects and demonstrates use cases of such rule graphs

Orchard spray study: a prediction model of droplet deposition states on leaf surfaces

During air-assisted spraying operations in orchards, the interaction between the droplets and the target leaves has a decisive influence on the retention of the droplets on the leaves and the final deposition state. Based on the observation of the final deposition effect of the droplets in the spray test, the retention state of the droplets on the leaves is divided into three categories: uniform distribution (hereinafter referred to as uniform), accumulation, and loss. During the initial interaction between the droplets and the leaves, the adhesion or sliding state of the droplets has an important influence on the final deposition state of the droplets, which is determined by the target leaf adhesion work in this paper. Based on obtaining the characteristic parameters of the leaf surface, a theoretical model of adhesion work related to parameters such as the contact angle, rough factor, and initial ilt angle of the leaf is established. Afterward, through the connection of the droplet coverage on the macro level, the establishment of the deposition state model of the droplet group on the leaf is completed. By conducting the experiment test based on the Box-Behnken design of response surface methodology (RSM), the droplet deposition states under the influence of the spray distance, fan outlet wind speed and droplet size were studied and compared with the predicted values. The test results show that the prediction accuracies of the three states of uniform, accumulation, and loss were 87.5%, 80%, and 100%, respectively. The results of the study indicate that the established prediction model can effectively predict the deposition states of droplets on leaves and provide a reference for the selection of spray operation parameters.Peer ReviewedPostprint (published version

On the Initialization of Graph Neural Networks

Graph Neural Networks (GNNs) have displayed considerable promise in graph representation learning across various applications. The core learning process requires the initialization of model weight matrices within each GNN layer, which is typically accomplished via classic initialization methods such as Xavier initialization. However, these methods were originally motivated to stabilize the variance of hidden embeddings and gradients across layers of Feedforward Neural Networks (FNNs) and Convolutional Neural Networks (CNNs) to avoid vanishing gradients and maintain steady information flow. In contrast, within the GNN context classical initializations disregard the impact of the input graph structure and message passing on variance. In this paper, we analyze the variance of forward and backward propagation across GNN layers and show that the variance instability of GNN initializations comes from the combined effect of the activation function, hidden dimension, graph structure and message passing. To better account for these influence factors, we propose a new initialization method for Variance Instability Reduction within GNN Optimization (Virgo), which naturally tends to equate forward and backward variances across successive layers. We conduct comprehensive experiments on 15 datasets to show that Virgo can lead to superior model performance and more stable variance at initialization on node classification, link prediction and graph classification tasks. Codes are in https://github.com/LspongebobJH/virgo_icml2023.Comment: Accepted by ICML 202

Study of flow resistance in open channels

River hydrodynamicsBed roughness and flow resistanc

Embodied Footprints: A Safety-guaranteed Collision Avoidance Model for Numerical Optimization-based Trajectory Planning

Numerical optimization-based methods are among the prevalent trajectory planners for autonomous driving. In a numerical optimization-based planner, the nominal continuous-time trajectory planning problem is discretized into a nonlinear program (NLP) problem with finite constraints imposed on finite collocation points. However, constraint violations between adjacent collocation points may still occur. This study proposes a safety-guaranteed collision-avoidance modeling method to eliminate the collision risks between adjacent collocation points in using numerical optimization-based trajectory planners. A new concept called embodied box is proposed, which is formed by enlarging the rectangular footprint of the ego vehicle. If one can ensure that the embodied boxes at finite collocation points are collide-free, then the ego vehicle's footprint is collide-free at any a moment between adjacent collocation points. We find that the geometric size of an embodied box is a simple function of vehicle velocity and curvature. The proposed theory lays a foundation for numerical optimization-based trajectory planners in autonomous driving.Comment: 12 pages, 13 figure