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12 results on '"Hu, Mengzhen"'

6. Effect of Indium Doping on Bias Stability in Dual-Target Co-Sputtering InZnSnO Thin-Film Transistors

Author

Zhang, Xinnan, Xu, Lei, Liang, Ruyu, Hu, Mengzhen, Ren, Jiali, Luo, Shijun, Song, Zengcai, Yang, Wenpeng, and Zhu, Zhihua

Abstract

Despite increasing indium content improves the field-effect mobility of metal–oxide thin-film transistors (TFTs), it is usually accompanied by the deterioration in bias stability. In this work, we achieve enhanced mobility and stability together within a certain range by controlling the sputtering power in the co-sputtering system. Due to the difference in electronegativity and binding energy between indium and zinc, the increase of indium content at lower sputtering power reduces the oxygen vacancy defects and smooth interfacial quality. In0.45Zn0.42Sn0.13O TFTs exhibit excellent stability threshold voltage variation of only 0.58 and −0.12 V under positive bias stress (PBS) and negative bias stress (NBS). However, excess indium could lead to high carrier concentrations in the channel, which introduced new defects, while defects between active and gate insulator (GI) layer interface trap electrons and lead to decreased stability. In addition, the maximum temperature in this work does not exceed 150 °C and the transmittance of all thin films is greater than 80% in the visible range, which has great potential for application in the next generation of transparent flexible electronics. These results indicate that optimizing the ratio of indium doping is an effective strategy for achieving simultaneous enhancement of mobility and stability.

Published
2023
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9. ReliabilitySPE

Author

Hu, Mengzhen, Sui, Jie, Yuan-Rui Zheng, Liu, Zheng, and Chuan-Peng, Hu

Subjects
FOS: Psychology, Multiverse, Psychology, Social and Behavioral Sciences, Reliability, Self-Perceptual Matching Task, Self-Prioritization Effect
Abstract

The self-prioritization effect (SPE) refers to the effect that performance on cognitive tasks is better when stimuli are related to the self than when they are not. In the last decade, the self -perceptual matching task (SPMT) has emerged as a mainstream paradigm for studying SPE due to its simplicity and elimination of familiarity effects. As a simple button-pressing task, SPMT yields two outcomes: reaction time and accuracy. Other indices can be derived from reaction times and accuracy, including sensitivity d prime under signal-detection theory, the efficiency index through a direct division between reaction times and accuracy, and drift rate (v) and starting point (z) estimated using drift-diffusion models. All these indices have been used to quantify SPE in the literature. However, the reliability of these SPE indices has never been tested. To fill this gap, we will re-analyze existing data from multiple datasets using intraclass correlation coefficient (ICC) and split-half reliability. Our results will provide a benchmark for future studies.

Published
2023
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10. Loan Default Prediction Based on Convolutional Neural Network and LightGBM.

Author

Zhu, Qiliang, Ding, Wenhao, Xiang, Mingsen, Hu, Mengzhen, and Zhang, Ning

Subjects
BANK loan default, CONSUMPTION (Economics), PREDICTION models, CONSUMERISM, ARTIFICIAL neural networks
Abstract

With the change of people's consumption mode, credit consumption has gradually become a new consumption trend. Frequent loan defaults give default prediction more and more attention. This paper proposes a new comprehensive prediction method of loan default. This method combines convolutional neural network and LightGBM algorithm to establish a prediction model. Firstly, the excellent feature extraction ability of convolutional neural network is used to extract features from the original loan data and generate a new feature matrix. Secondly, the new feature matrix is used as input data, and the parameters of LightGBM algorithm are adjusted through grid search so as to build the LightGBM model. Finally, the LightGBM model is trained based on the new feature matrix, and the CNN-LightGBM loan default prediction model is obtained. To verify the effectiveness and superiority of our model, a series of experiments were conducted to compare the proposed prediction model with four classical models. The results show that CNN-LightGBM model is superior to other models in all evaluation indexes. [ABSTRACT FROM AUTHOR]

Published
2023
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12. Business Decision-Making Game Facilitates Training Effectiveness: A Three-Level Meta-Analysis.

Author

Cai, Yingni, Liu, Zheng, Hu, Mengzhen, Pang, Tianxing, LUO, Wenjing, Cheng, Juanxi, and WEI, Yutong

Abstract

Business decision-making games are designed to replicate real-world business scenarios, challenging trainees to make strategic decisions in management, finance, marketing, and strategic planning. Despite their widespread use, a comprehensive evaluation of the training effectiveness of these games in business educational settings is lacking. To address this gap, we conducted a three-level meta-analysis to estimate the effectiveness of business decision-making games and identified the conditions under which these games are most effective. We focused on four dimensions of training effectiveness: reactions, learning process, learning performance, and motivation. We identified a total of 41 primary studies utilizing a between-subjects experimental design (N = 8376), yielding 94 effect sizes. The results demonstrated that business decision-making games effectively enhance management training effectiveness (g =.53, 95% CI [.35,.71]), resulting in improvements across reactions (g =.44), learning process (g =.66), learning performance (g =.45), and motivations (g =.85). This positive effect remained consistent and robust across a wide variety of conditions, including participant characteristics, game features, implementation methods, and research designs. Practical and theoretical implications for business education are discussed. [ABSTRACT FROM AUTHOR]

Published
2024
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