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42 results on '"Dong, Xueying"'

5. RUNDC1 inhibits autolysosome formation and survival of zebrafish via clasping ATG14-STX17-SNAP29 complex

Author

Zhang, Rui, Yang, Yuyan, He, Chao, Zhang, Xin, Torraca, Vincenzo, Wang, Shen, Liu, Nan, Yang, Jiaren, Liu, Shicheng, Yuan, Jinglei, Gou, Dongzhi, Li, Shi, Dong, Xueying, Xie, Yufei, He, Junling, Bai, Hua, Hu, Mengyu, Liao, Zhiquan, Huang, Yuan, Lyu, Hao, Xiao, Shuai, Guo, Dong, Ali, Declan William, Michalak, Marek, Ma, Cong, Chen, Xing-Zhen, Tang, Jingfeng, and Zhou, Cefan

Published
2023
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10. Exploration of Civics Teaching Mode in Colleges and Universities Relying on Digital Classroom Platforms

Author

Dong Xueying and Liu Guizhen

Subjects
cda cognitive analysis, k-means algorithm, interaction behavior characteristics, digital classroom, civics teaching mode, 93c62, Mathematics, QA1-939
Abstract

The digital teaching model of Civics at the current stage still has deficiencies and does not achieve the expected effect. This paper utilizes data analysis and mining methods starting from the digital classroom platform to assess students’ knowledge and skill mastery through the CDA static cognitive analysis model. Then, students’ learning behaviors were analyzed using K-Means, and an evaluation model of interactive behavioral characteristics was constructed to analyze students’ Civics learning characteristics in combination with the above three aspects. Relying on the digital classroom platform, a corresponding teaching model for Civics in colleges and universities was developed. Our analysis of the impact of teachers’ and students’ interaction behaviors in Civics teaching and the realistic results of the Civics teaching model designed in this study is aided by the selection of empirical samples. The results show that the optimized students’ interactive behavior rate of Civics learning is more than 50%, and the interaction time increases to 19 minutes, which has a significant improvement effect. Analyzing the digital classroom teaching model of Civics, 28 students in Case 2 in the course comparison had greater interest in Civics learning, meanwhile, only 6 students had low interest in Civics learning, and the digital teaching model of Civics effectively enhanced students’ knowledge and literacy.

Published
2024
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32. Phosphorylated STYK1 restrains the inhibitory role of EGFR in autophagy initiation and EGFR-TKIs sensitivity

Author

Zhou, Cefan, primary, Dong, Xueying, additional, Wang, Ming, additional, Qian, Xuehong, additional, Hu, Miao, additional, Liang, Kai, additional, Liang, Yanyan, additional, Zhang, Rui, additional, Huang, Yuan, additional, Lyu, Hao, additional, Xiao, Shuai, additional, Tang, Yongfei, additional, Ali, Declan William, additional, Michalak, Marek, additional, Chen, Xing-Zhen, additional, and Tang, Jingfeng, additional

Published
2022
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33. Prediction of PM2.5 concentration considering temporal and spatial features: A case study of Fushun, Liaoning Province

Author

Dong Xueying, Ma Xiaohe, and Fei Lei

Subjects
Statistics and Probability, 0209 industrial biotechnology, 020901 industrial engineering & automation, Artificial Intelligence, General Engineering, Environmental science, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, 0105 earth and related environmental sciences
Abstract

With the development of the urban industry in recent years, air pollution in areas such as factories and streets has become more and more serious. Air quality problems directly affect the normal lives of residents. Effectively predicting the future air condition in the area through relevant historical data has high application value for early warning of this area. Through the study of the previous monitoring data, it is found that the pollutant data of adjacent monitoring stations are correlated in more periods. Therefore, this paper proposes a hybrid model based on CNN and Bi-LSTM, using CNN to synthesize multiple adjacent stations with strong correlations to extract spatial features between data, and using Bi-LSTM to extract features in the time dimension to finally achieve pollutant concentration prediction. Using the historical data of 40 monitoring stations in different locations of Fushun city to conduct research. By comparing with the traditional prediction model, the results prove that the model proposed in this paper has higher accuracy and stronger robustness.

Published
2020
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37. Flexible quasi-solid-state zinc-ion hybrid supercapacitor based on carbon cloths displays ultrahigh areal capacitance

Author

Zhang, Yifu, Wang, Peng, Dong, Xueying, Jiang, Hanmei, Cui, Miao, and Meng, Changgong

Abstract

•A ZHSC with ultrahigh areal capacitance and long stability was demonstrated by an ACC cathode.•The ACC//Zn@ACC ZHSC device had the areal capacitance up to 2437 mF•cm−2at 1 mA•cm−2.•The FQSS ACC//Zn@ACC ZHSC device had high mechanical flexibility and wearable stability and output.•The energy storage mechanism was double-layer capacitive behavior of Zn2+accompanying dissolution/deposition of ZSO.

Published
2023
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41. Automatic Identification Of Construction Dust Based On Improved K-Means Algorithm

Author

Ma Xiaohe, Fei Lei, and Dong Xueying

Subjects
Identification (information), business.industry, k-means clustering, Environmental science, Pattern recognition, Artificial intelligence, business
Abstract

At present, the construction dust has caused great losses to people's healthy life and national economic development. In order to solve the shortcomings of the existing real-time sensor network detection of construction dust such as poor accuracy, this paper proposes an automatic identification of construction dust based on computer vision and improved K-Means clustering algorithm. We extract the saturation of HSV color model of each image to form a text data set. We determine the median value of the data set as the initial centroid of clustering, reduce the number of iterations and get the global optimal solution. Mahalanobis distance is used as similarity measure to cluster, which reduces the difference between different feature measures, improves the accuracy, and realizes the automatic recognition of construction dust based on computer vision. For the improved K-Means algorithm, the precision, recall rate and harmonic mean value are used to analyze the clustering results. The experimental results show that the improved K-Means algorithm has good robustness and high accuracy, and the automatic recognition rate can reach 89.33%.

Published
2021
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42. Tailoring NH4+storage by regulating oxygen defect in ammonium vanadate

Author

Liu, Yanyan, Feng, Ziyi, Jiang, Hanmei, Dong, Xueying, Meng, Changgong, and Zhang, Yifu

Abstract

Defect engineering is an effective strategy for modifying the energy storage materials to improve their electrochemical performance. However, the impact of oxygen defect and its content on the electrochemical performances in the burgeoning aqueous NH4+storage field remains explored. Therefore, for the first time in this work, an oxygen-defective ammonium vanadate [(NH4)2V10O25·8H2O, denoted as Od-NHVO] with a novel 3D porous flower-like architecture was achieved viathe reduction of thiourea in a mild reaction condition, which is a facile method that can realize the intention to regulate the oxygen defect content, with the capability of mass-production. The as-prepared OdM-NHVO with moderate oxygen defect content can deliver a stable specific capacitance output (505 F g-1, 252 mAh g−1 at 0.5 A g−1with ∼80% capacitance retention after 10,000 cycles), which benefits from extra active sites, unimpeded NH4+-migration path and relatively high structure integrity. In contrast, low oxygen defect content will lead to the torpid electrochemical reaction kinetics while too high content of it will reduce the charge-storage capability and induce structural disintegration. The superior NH4+-storage behavior is achieved with the reversible intercalation/de-intercalation process of NH4+accompanied by forming/breaking of hydrogen bond. As expected, the assembled flexible OdM-NHVO//PTCDI quasi-solid-state hybrid supercapacitor (FQSS HSC) also exhibits high areal capacitance, energy density and reliable flexibility. This work provides a new avenue for developing materials with oxygen-deficient structure for application in various aqueous non-metal cation storage systems.

Published
2023
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