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935 results on '"Zhang, Ruizhi"'

151. Immune-Boosting Effect of the COVID-19 Vaccine: Real-World Bidirectional Cohort Study

Author

Liu, Ming, primary, Zhao, Tianshuo, additional, Mu, Qiuyue, additional, Zhang, Ruizhi, additional, Liu, Chunting, additional, Xu, Fei, additional, Liang, Luxiang, additional, Zhao, Linglu, additional, Zhao, Suye, additional, Cai, Xianming, additional, Wang, Mingting, additional, Huang, Ninghua, additional, Feng, Tian, additional, Lei, Shiguang, additional, Yang, Guanghong, additional, and Cui, Fuqiang, additional

Published
2023
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152. Endothelial DGKG promotes tumor angiogenesis and immune evasion in hepatocellular carcinoma

Author

Zhang, Liren, primary, Xu, Jiali, additional, Zhou, Suiqing, additional, Yao, Feifan, additional, Zhang, Ruizhi, additional, You, Wenhua, additional, Yu, Kai, additional, Zhang, Yu, additional, Baheti, Tasiken, additional, Pu, Liyong, additional, Xu, Jing, additional, Qian, Xiaofeng, additional, Xia, Yongxiang, additional, Dai, Xinzheng, additional, Li, Qing, additional, and Wang, Xuehao, additional

Published
2023
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157. Targeting BCL6 in Gastrointestinal Stromal Tumor Promotes p53-Mediated Apoptosis to Enhance the Antitumor Activity of Imatinib

Author

Zeng, Xiangyu, primary, Zhao, Fei, additional, Jia, Jie, additional, Ma, Xianxiong, additional, Jiang, Qi, additional, Zhang, Ruizhi, additional, Li, Chengguo, additional, Wang, Tao, additional, Liu, Weizhen, additional, Hao, Yalan, additional, Tao, Kaixiong, additional, Lou, Zhenkun, additional, and Zhang, Peng, additional

Published
2023
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161. Essays on Experimental Evidence of Peer Influence

Author

Zhang, Ruizhi

Subjects
Economics, Altruism, Philanthropy, Inter-generational Transfers, Field Experiments, Laboratory, Individual Behavior, Marketing, Matching Theory
Abstract

This dissertation presents three experimental studies with an emphasis on peer influence in people’s behavior. The first chapter studies a mechanism that facilitates team formation and cooperation among peers. The second and third chapters focus on peer influence in altruistic behavior such as donations to charities and street performers. The first chapter implements a field experiment in a digital environment with a matching mechanism that facilitates user-team assortativity. Using machine learning techniques, our system identifies users with high expected values and provides them the option to join highly active social groups in terms of engagement and expenditure. We deploy this mechanism experimentally in a popular online game. We find that assortative matching significantly increases new user engagement and productivity, and improves the overall health of the community. New users who join more active communities do exhibit higher engagement and productivity, but do not spend more money. Revenue, however, does increase as existing members of high-activity teams react positively to the influx of new, better-quality users. Teams matched with low-quality new users are negatively impacted leading to a more segregated team environment. We discuss implications of these findings both from a profit-maximizing firm’s as well as a broader societal perspective. The second chapter provides laboratory evidence on how different levels of monetary rewards affect image motivations in the context of charitable donations. We specifically study the interaction effect of image and material motivations on donation decisions. We implement a within-subject design with two treatments: monetary incentive and donation visibility. We have three levels of monetary incentives: no incentive (0% rebate), a low incentive (10% rebate), and a high incentive (50% rebate). We also have three levels in donation visibility: private donation, public donation without costly non-disclosure (ND) option, and public donation with costly non-disclosure option. Our results show that a small reward in terms of a 10% donation rebate does not impose any significant donation behavior changes, neither in private nor in public. When a large reward is given (50% donation rebate), people’s perception of the monetary reward determines whether the reward crowds out donations. For those who believe the reward makes their donations appear ‘less generous’, a 50% rebate significantly crowds out charitable donations in public. For those who do not associate monetary rewards with a negative image, a high rebate significantly increases their donations in public. We also find that males, in general, are more sensitive to their public images and significantly reduce their donations in public when offered a high reward, while the effects on females are non-significant. The third chapter evaluates the impact of peers’ giving behavior on people’s willingness to donate. We implement a field experiment studying donations to street performers at stoplights on the streets of Lima, Perú. The treatment condition is defined as observing another vehicle making a donation. We use natural variation in donations made by passing-by drivers, as well as experimentally-manipulated variation in donations made by hired drivers. We study how observing a donation from another vehicle affects the probability and magnitude of donations of treated vehicles. Our experiment results indicate a strong substitution effect of peer influence. When drivers observe another vehicle donate, they are significantly less likely to make a donation to the same performer and overall average donations are lower as well. Our study contributes to the literature by bringing evidence of peer influence in donations in a developing world context.

Published
2020

164. Exploring the ex-situ components within Gaia DR3.

Author

Li, Zhuohan, Zhao, Gang, Zhang, Ruizhi, Xue, Xiang-Xiang, Chen, Yuqin, and Amarante, João A S

Subjects
STAR clusters, MAGELLANIC clouds, MILKY Way, BINARY stars, DEEP learning
Abstract

The presence of Gaia DR3 provides a large sample of stars with complete 6D information, offering a fertile ground for the exploration of stellar objects that were accreted to the Milky Way through ancient merger events. In this study, we developed a deep learning methodology to identify ex-situ stars within the Gaia DR3 catalogue. After two phases of training, our neural network (NN) model was capable of performing binary classification of stars based on input data consisting of 3D position and velocity, as well as actions. From the target sample of 27 085 748 stars, our NN model managed to identify 160 146 ex-situ stars. The metallicity distribution suggests that this ex-situ sample comprises multiple components but appears to be predominated by the Gaia-Sausage-Enceladus (GSE). We identified member stars of the Magellanic Clouds, Sagittarius, and 20 globular clusters throughout our examination. Furthermore, an extensive group of member stars from GSE, Thamnos, Sequoia, Helmi streams, Wukong, and Pontus were meticulously selected, constituting an ideal sample for the comprehensive study of substructures. Finally, we conducted a preliminary estimation to determine the proportions of ex-situ stars in the thin disc, thick disc, and halo, which resulted in percentages of 0.1 per cent, 1.6 per cent, and 63.2 per cent, respectively. As the vertical height from the Galactic disc and distance from the Galactic centre increased, there was a corresponding upward trend in the ex-situ fraction of the target sample. [ABSTRACT FROM AUTHOR]

Published
2024
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165. Enhancing laser-driven flyer velocity by optimizing of modulation period of Al/Ti reactive multilayer films.

Author

Gao, Weilong, Zhang, Ruizhi, Wang, Jin, Huang, Zihao, Li, Zhiguo, Fu, Yajun, Luo, Guoqiang, and Tu, Rong

Subjects
MOLECULAR dynamics, VELOCITY, KINETIC energy, ELECTROMAGNETIC interference, NANOSTRUCTURED materials
Abstract

Reactive multilayer films (RMFs), a type of nanostructured energetic material, are recognized as an indispensable component for laser-driven flyer plate initiator systems. In this work, Al/Ti-RMF with three different modulation periods (600, 300, and 150 nm) were prepared and integrated into multilayer flyer plates, and energetic material with optimized performance for laser-driven flyers was obtained. Cross-sectional observations demonstrate that the modulation periods of the RMF are precisely regulated, with thickness errors falling within 3.4%. The velocity of the flyer plates was significantly higher with a modulation period of 150 nm, reaching 2174.16 m/s. Molecular dynamics simulation results show that as the modulation period decreases, the diffusion rate of atoms increases, enabling the reaction between the RMF to be completed in a shorter time span, which makes for higher velocity of the flyer. The energy coupling efficiency results indicate that the kinetic energy coupling efficiency of the RMF with a modulation period of 150 nm is 145.6% and 29.8% higher compared to those with modulation periods of 600 and 300 nm, respectively. It is proved that Al/Ti-RMF have high-energy output performance and can be a novel candidate for laser-driven flyer plates, which will play a critical role in complex electromagnetic interference environments in the future. [ABSTRACT FROM AUTHOR]

Published
2023
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168. Estrogen ameliorates sepsis-induced vascular hyporeactivity in thoracic aorta of female rats via permissive effect of GRα expression

Author

Wang, Shan, primary, Wu, Jue, additional, Yang, Kai, additional, Liu, Chunlei, additional, Li, Xin, additional, Wu, Liben, additional, Qi, Xiaoqin, additional, Zhang, Ruizhi, additional, Ni, Wenfeng, additional, Pei, Jinlian, additional, Gu, Fangyan, additional, Lu, Bing, additional, Wang, Yan, additional, and Tian, Yaping, additional

Published
2023
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174. Immune-Boosting Effect of the COVID-19 Vaccine: Real-World Bidirectional Cohort Study (Preprint)

Author

Liu, Ming, primary, Zhao, Tianshuo, additional, Mu, Qiuyue, additional, Zhang, Ruizhi, additional, Liu, Chunting, additional, Xu, Fei, additional, Liang, Luxiang, additional, Zhao, Linglu, additional, Zhao, Suye, additional, Cai, Xianming, additional, Wang, Mingting, additional, Huang, Ninghua, additional, Feng, Tian, additional, Lei, Shiguang, additional, Yang, Guanghong, additional, and Cui, Fuqiang, additional

Published
2023
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176. Exploring the ex-situ components within Gaia DR3

Author

Li, Zhuohan, Zhao, Gang, Zhang, Ruizhi, Xue, Xiang-Xiang, Chen, Yuqin, Amarante, João A S, Li, Zhuohan, Zhao, Gang, Zhang, Ruizhi, Xue, Xiang-Xiang, Chen, Yuqin, and Amarante, João A S

Abstract

The presence of Gaia DR3 provides a large sample of stars with complete 6D information, offering a fertile ground for the exploration of stellar objects that were accreted to the Milky Way through ancient merger events. In this study, we developed a deep learning methodology to identify ex-situ stars within the Gaia DR3 catalogue. After two phases of training, our neural network (NN) model was capable of performing binary classification of stars based on input data consisting of 3D position and velocity, as well as actions. From the target sample of 27 085 748 stars, our NN model managed to identify 160 146 ex-situ stars. The metallicity distribution suggests that this ex-situ sample comprises multiple components but appears to be predominated by the Gaia-Sausage-Enceladus. We identified member stars of the Magellanic Clouds, Sagittarius, and 20 globular clusters throughout our examination. Furthermore, an extensive group of member stars from Gaia-Sausage-Enceladus, Thamnos, Sequoia, Helmi streams, Wukong, and Pontus were meticulously selected, constituting an ideal sample for the comprehensive study of substructures. Finally, we conducted a preliminary estimation to determine the proportions of ex-situ stars in the thin disc, thick disc, and halo, which resulted in percentages of 0.1%, 1.6%, and 63.2%, respectively. As the vertical height from the Galactic disc and distance from the Galactic centre increased, there was a corresponding upward trend in the ex-situ fraction of the target sample.

Published
2023

181. Rapid online plant leaf area change detection with high-throughput plant image data.

Author

Zhan, Yinglun, Zhang, Ruizhi, Zhou, Yuzhen, Stoerger, Vincent, Hiller, Jeremy, Awada, Tala, and Ge, Yufeng

Subjects
*LEAF area, *FOLIAGE plants, *PLANT breeding, *CROP management, *FALSE alarms
Abstract

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis. [ABSTRACT FROM AUTHOR]

Published
2023
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199. 3D Superlattice Ceramics of

Author

Zhang, Ruizhi, Koumoto, Kunihito, Hull, Robert, Series editor, Jagadish, Chennupati, Series editor, Osgood, Richard M., Series editor, Parisi, Jürgen, Series editor, Wang, Zhiming M., Series editor, Koumoto, Kunihito, editor, and Mori, Takao, editor

Published
2013
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