A STUDY ON PROBLEMS AND SOLUTIONS OF PRIVATE UNIVERSITY’S ENGLISH TEACHING – A CASE STUDY OF A BEIJING PRIVATE UNIVERSITY

Aims: As a part of university education, college English teaching in private universities is important. The aim of this article is to conduct a survey about the English teaching in these private universities, trying to find out the problems in teaching and learning of English in these private universities, and analyze the deep reasons behind these problems, based on the teaching and learning of English at a university in Beijing.Study Design: This research has been undertaken both from qualitative (mainly from related theories and researches) and quantitative studies (including questionnaire survey, and interviews with teachers and students). The qualitative study could give a basic understanding about the teaching and learning of English  conditions in private universities, while the quantitative research was aimed at getting precise statistics  which could be used for  evaluating whether the results from qualitative study were right or wrong. Data collection involved several research methods, such as document analysis, questionnaire survey, and interviews with teachers and students. Then, a deep analysis of the data was conducted to find out the problems existing regarding college teaching of English at a private university and to analyze the reasons behind these problems. Finally, the author suggested some solutions to solve these problems.Methodology: 1) Document analysis: the author could not come into contact with all private universities’ administrators to investigate this research and the author then turned to analyze their published papers in order to find out teaching and learning of English conditions in private colleges. 2) Interviews: the author interviewed some teachers and administration-related people about the English teaching and learning in a Beijing private university. 3) Questionnaire: the author conducted a questionnaire survey among 200 students about their satisfactory level on their teaching and learning of English.Results: Although private universities have made great improvements in teaching and learning of English, problems (such as unclear teaching objectives, the lack of scientific teaching methods, the shortage of advanced teachers, and students lacking in study motivation) still exist.Conclusion: We can solve these problems through the related theories and work together with the related people.    Article visualizations

LncRNA HOTAIR promotes MPP+-induced neuronal injury in Parkinson’s disease by regulating the miR-874-5p/ATG10 axis

Parkinson’s disease (PD) is a neurodegenerative disease caused by the loss of dopaminergic neurons. Long non-coding RNAs (lncRNAs) play an important role in many neurological diseases, including PD. This study aimed to investigate the role of lncRNA HOX transcript antisense RNA (HOTAIR) in PD pathogenesis and its potential mechanism. SK-N-SH cells were exposed to 1-methyl-4-phenylpyridinium (MPP+) to mimic PD model in vitro. The levels of HOTAIR, miR-874-5p and autophagy-related 10 (ATG10) were determined by quantitative real-time polymerase chain reaction (qRT-PCR) or western blot assay. Cell viability and apoptosis were assessed by Cell Counting Kit-8 (CCK-8) assay and flow cytometry. The expression of apoptosis-related proteins was measured by western blot. The levels of neuroinflammation-related factors were detected by enzyme-linked immunosorbent assay (ELISA). Commercial kits was used to monitor lactate dehydrogenase (LDH) activity, reactive oxygen (ROS) generation and superoxide dismutase (SOD) activity. The interaction among HOTAIR, miR-874-5p and ATG10 were verified by dual-luciferase reporter assay or RNA immunoprecipitation (RIP) assay. HOTAIR and ATG10 were up-regulated, and miR-874-5p was down-regulated in dose- and time-dependent manners in MPP+-treated SK-N-SH cells. HOTAIR knockdown reduced MPP+-induced neuronal damage. HOTAIR aggrandized MPP+-triggered neuronal injury by sponging miR-874-5p. Also, miR-874-5p attenuated MPP+-triggered neuronal damage by targeting ATG10. Moreover, HOTAIR regulated ATG10 expression via sponging miR-874-5p. HOTAIR promoted MPP+-induced neuronal injury via modulating the miR-874-5p/ATG10 axis in SK-N-SH cells

WOS-ELM-Based Double Redundancy Fault Diagnosis and Reconstruction for Aeroengine Sensor

In order to diagnose sensor fault of aeroengine more quickly and accurately, a double redundancy diagnosis approach based on Weighted Online Sequential Extreme Learning Machine (WOS-ELM) is proposed in this paper. WOS-ELM, which assigns different weights to old and new data, implements weighted dealing with the input data to get more precise training models. The proposed approach contains two series of diagnosis models, that is, spatial model and time model. The application of double redundancy based on spatial and time redundancy can in real time detect the hard fault and soft fault much earlier. The trouble-free or reconstructed time redundancy model can be utilized to update the training model and make it be consistent with the practical operation mode of the aeroengine. Simulation results illustrate the effectiveness and feasibility of the proposed method

Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers

On the special oxidation mechanism of a Mg-Y-Al alloy contained LPSO phase at high temperatures

This work investigated the oxidation of Mg-11Y-1Al alloy in Ar-20%O2 at 500{\deg}through multiscale characterization. The results show that the network-like long-period stacking ordered(LPSO) phase decomposed into a needle-like LPSO phase and a polygonal Mg24Y5 phase. The needle-like LPSO phase resulted in the formation of a high-dense of needle-like oxide at the oxidation front of the area initially occupied by the network-like LPSO phase. The further inward oxygen would diffuse along the needle-like oxide-matrix interfaces and react with Y in the surrounding Mg matrix, resulting in the lateral growth of these needle-like oxides. Finally, the discrete needle-like oxides were interconnected to form a thicker and continuous oxide scale which could be more effective in hindering the elemental diffusion. Meanwhile, Al could partially enter the Y2O3 oxide scale and formed a strengthened (Y,Al)O oxide scale which could show a greater resistance to cracking and debonding

N′-(3,5-Dichloro-2-hy­droxy­benzyl­idene)-4-(dimethyl­amino)­benzohydrazide methanol monosolvate

The title compound, C16H15Cl2N3O2·CH3OH, a Schiff base molecule, is prepared by the reaction of 3,5-dichloro­salicyl­aldehyde with 4-dimethyl­amino­benzohydrazide in methanol. The Schiff base mol­ecule is approximately planar, with a mean deviation from the least-squares plane defined by the non-H atoms of 0.0452 (3) Å, and with a dihedral angle between the benzene rings of 4.2 (3)°. This planarity is assisted by the formation of an intra­molecular O—H⋯N hydrogen bond. In the crystal, adjacent Schiff base mol­ecules are linked by two methanol mol­ecules through N—H⋯O and O—H⋯O hydrogen bonds, forming dimers

LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment

For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications. In this paper, we present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation without any limitation of light conditions and wearable devices. In particular, we design a distillation mechanism to mitigate the distribution-varying effect of LiDAR point clouds and exploit the temporal-spatial geometric and dynamic information existing in consecutive frames to solve the occlusion and noise disturbance. LiveHPS, with its efficient configuration and high-quality output, is well-suited for real-world applications. Moreover, we propose a huge human motion dataset, named FreeMotion, which is collected in various scenarios with diverse human poses, shapes and translations. It consists of multi-modal and multi-view acquisition data from calibrated and synchronized LiDARs, cameras, and IMUs. Extensive experiments on our new dataset and other public datasets demonstrate the SOTA performance and robustness of our approach. We will release our code and dataset soon.Comment: Accepted by CVPR 202