A study of particles looseness in screening process of a linear vibrating screen

We investigated the movement of particles in screening process over the sieve plate of a linear vibrating screen using the Discrete Element Method (DEM). The behavior of particles which is affected by a series of vibrational parameters including amplitude, frequency and vibration direction angle determining screening performance. This paper centers on particles looseness by analyzing the looseness coefficient and looseness rate. The relationships between the looseness coefficient, looseness rate and vibration parameters were profoundly discussed. Mathematical models relating looseness coefficient to time were established using the least squares method. An experimental platform which combines high-speed camera system with experimental prototype of vibrating screen was designed. The research made a more in-depth investigation of particles’ movements and analysis of particle looseness. Physical experiments were used to verify the reliability of simulation results. Finally, we would come into the following conclusions: high frequency and large amplitude make particles obtain more energy to be active and the average distances among particles get larger slowly. On the contrary, at low frequency and amplitude, the looseness coefficient and looseness rate were relatively low. When the amplitude approaches 2.7 mm, the frequency is about 34 Hz and the vibration angle is around 42 degrees, the looseness ratio produces better performance. The paper offered insights to the design and manufacturing of vibrating screen

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a hypergraph convolutional network to improve SBR. Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task. Since the two types of networks both are based on hypergraph, which can be seen as two channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel Hypergraph Convolutional Networks). Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the results validate the effectiveness of hypergraph modeling and self-supervised task. The implementation of our model is available at https://github.com/xiaxin1998/DHCNComment: 9 pages, 4 figures, accepted by AAAI'2

Analysis on the Risk and Supervision of P2P Online Financing Platforms in China

Microcredit is a vital breakthrough to solve the financial problems of low-income groups and small and medium-sized enterprises, while traditional microfinance providers can only meet a small proportion of their capital needs. By using internet technology, P2P online financing extends the innovative development of microcredit with the aim of solving traditional micro-credit problems. This paper mainly explores the existing online financing operation model of P2P in China, and summarizes the relevant problems, such as low entry barriers for P2P online financing enterprises and lack of supervision, Lack of verification on the qualification of borrowers and poor management of the platform, imperfect information revealed or providing false information by platform, etc. Finally, the article put forward some suggestions concerning the healthy development for the P2P online financing platform, including the establishing entry audit system and strengthening the supervision of the P2P platform, strengthening the management of borrowers and improving the credit collection system, and strengthening the disclosure of information by platform