A Locking-Free Weak Galerkin Finite Element Method for Linear Elasticity Problems

In this paper, we introduce and analyze a lowest-order locking-free weak Galerkin (WG) finite element scheme for the grad-div formulation of linear elasticity problems. The scheme uses linear functions in the interior of mesh elements and constants on edges (2D) or faces (3D), respectively, to approximate the displacement. An $H(div)$-conforming displacement reconstruction operator is employed to modify test functions in the right-hand side of the discrete form, in order to eliminate the dependence of the $Lam\acute{e}$ parameter $\lambda$ in error estimates, i.e., making the scheme locking-free. The method works without requiring $\lambda \|\nabla\cdot \mathbf{u}\|_1$ to be bounded. We prove optimal error estimates, independent of $\lambda$, in both the $H^1$-norm and the $L^2$-norm. Numerical experiments validate that the method is effective and locking-free

Research on effectiveness of college english blended teaching mode under small private online course based on machine learning

Article highlights Based on relevant literature, the study constructs the theoretical dimensions of the students’ learning effectiveness of college English based on the SPOC blended teaching mode. After the steps of item development, draft scale development, data collection of pretest scale, item analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis and validity analysis, the scale on the learning effectiveness of college English is developed, which provides a reference tool for subsequent related studies. The study analyzes and evaluates students’ learning effectiveness based on machine learning in order to guide students’ learning behavior and improve the quality of students’ learning effectiveness. The results obtained by machine learning show that learning effectiveness are improved in the SPOC-based teaching mode. It helps teachers know the learning effectiveness of their students at different learning stages and change their teaching strategies for improving students’ learning effectiveness in time. Based on the analysis of data on students’ learning effectiveness, the influencing factors that affect students’ learning effectiveness could be analyzed. The suggestions of improvement of the SPOC-based blended teaching mode of college English are proposed from the influencing factors, so as to improve the teaching effectiveness and students’ learning effectiveness

Factors Affecting Road Rating

The decision of traffic congestion degree is an important research topic today. In severe traffic jams, the speed of the car is slow, and the speed estimate is very inaccurate.This paper first uses the data collected by Google Maps to reclassify road levels by using analytic hierarchy process. The vehicle speed, road length, normal travel time, traffic volume, and road level are selected as the input features of the limit learning machine, and the delay coefficient is selected. As the limit learning machine as the output value, 10-fold cross-validation is used. Compared with the traditional neural network, it is found that the training speed of the limit learning machine is 10 times that of the traditional neural network, and the mean square error is 0.8 times that of the traditional neural network. The stability of the model Significantly higher than traditional neural networks.Finally, the delay coefficient predicted by the extreme learning machine and the normal travel time are combined with the knowledge of queuing theory to finally predict the delay time

Hydrogenation and hydrodeoxygenation of biomass-derived oxygenates to liquid alkanes for transportation fuels

An attractive approach for the production of transportation fuels from renewable biomass resources is to convert oxygenates into alkanes. In this paper, C5–C20 alkanes formed via the hydrogenation and hydrodeoxygenation of the oligomers of furfuryl alcohol(FA) can be used as gasoline, diesel and jet fuel fraction. The first step of the process is the oligomers of FA convert into hydrogenated products over Raney Ni catalyst in a batch reactor. The second step of the process converts hydrogenated products to alkanes via hydrodeoxygenation over different bi-functional catalysts include hydrogenation and acidic deoxidization active sites. After this process, the oxygen content decreased from 22.1 wt% in the oligomers of FA to 0.58 wt% in the hydrodeoxygenation products