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657 results on '"Zhang, Shengrui"'

101. The nutritional composition of the vegetable soybean (maodou) and its potential in combatting malnutrition

Author

Agyenim-Boateng, Kwadwo Gyapong, primary, Zhang, Shengrui, additional, Zhang, Shibi, additional, Khattak, Aimal Nawaz, additional, Shaibu, Abdulwahab, additional, Abdelghany, Ahmed M., additional, Qi, Jie, additional, Azam, Muhammad, additional, Ma, Caiyou, additional, Feng, Yue, additional, Feng, Huoyi, additional, Liu, Yitian, additional, Li, Jing, additional, Li, Bin, additional, and Sun, Junming, additional

Published
2023
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105. Dual‐function C2H2‐type zinc‐finger transcription factor GmZFP7 contributes to isoflavone accumulation in soybean

Author

Feng, Yue, primary, Zhang, Shengrui, additional, Li, Jing, additional, Pei, Ruili, additional, Tian, Ling, additional, Qi, Jie, additional, Azam, Muhammad, additional, Agyenim‐Boateng, Kwadwo Gyapong, additional, Shaibu, Abdulwahab S., additional, Liu, Yitian, additional, Zhu, Zuofeng, additional, Li, Bin, additional, and Sun, Junming, additional

Published
2022
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109. Profiling seed soluble sugar compositions in 1164 Chinese soybean accessions from major growing ecoregions

Author

Qi, Jie, primary, Zhang, Shengrui, additional, Azam, Muhammad, additional, Shaibu, Abdulwahab S., additional, Abdelghany, Ahmed M., additional, Feng, Yue, additional, Huai, Yuanyuan, additional, Feng, Huoyi, additional, Liu, Yitian, additional, Ma, Caiyou, additional, Gebregziabher, Berhane S., additional, Ghosh, Suprio, additional, Li, Jing, additional, Yu, Deyue, additional, Li, Bin, additional, Qiu, Lijuan, additional, and Sun, Junming, additional

Published
2022
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112. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver's Sensitivity to Them

Author

Jiao, Shuaiyang, Zhang, Shengrui, Li, Zongzhi, Zhou, Bei, and Zhao, Dan

Subjects
United States. Federal Highway Administration
Abstract

This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver's sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process., 1. Introduction In the last half-century, extensive research has been conducted worldwide to develop traffic flow models aimed to explain the nature of traffic flow characteristics and reveal the internal [...]

Published
2020
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117. A general model for “germplasm-omics” data sharing and mining: a case study of SoyFGB v2.0

Author

Zheng, Tianqing, primary, Li, Yinghui, additional, Li, Yanfei, additional, Zhang, Shengrui, additional, Ge, Tianli, additional, Wang, Chunchao, additional, Zhang, Fan, additional, Faruquee, Muhiuddin, additional, Zhang, Lina, additional, Wu, Xiangyun, additional, Tian, Yu, additional, Jiang, Shan, additional, Xu, Jianlong, additional, and Qiu, Lijuan, additional

Published
2022
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121. Dynamic Behavior Analysis of Discrete Neural Networks with Delay

Author

Ma, Runnian, Lei, Sheping, Zhang, Shengrui, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Liao, Xiaofeng, editor, and Yi, Zhang, editor

Published
2005
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122. Stability Conditions for Discrete Neural Networks in Partial Simultaneous Updating Mode

Author

Ma, Runnian, Zhang, Shengrui, Lei, Sheping, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Liao, Xiaofeng, editor, and Yi, Zhang, editor

Published
2005
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125. Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Author

Luo, Xianglong, Li, Danyang, Yang, Yu, and Zhang, Shengrui

Subjects
Neural network, Neural networks -- Rankings -- Analysis
Abstract

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement., 1. Introduction The accurate prediction of future traffic conditions (e.g., traffic flow, travel speed and travel time) is crucial requirement for Intelligent Transportation Systems (ITS), which can help administrators take [...]

Published
2019
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126. Dual‐function C2H2‐type zinc‐finger transcription factor GmZFP7 contributes to isoflavone accumulation in soybean.

Author

Feng, Yue, Zhang, Shengrui, Li, Jing, Pei, Ruili, Tian, Ling, Qi, Jie, Azam, Muhammad, Agyenim‐Boateng, Kwadwo Gyapong, Shaibu, Abdulwahab S., Liu, Yitian, Zhu, Zuofeng, Li, Bin, and Sun, Junming

Subjects
*TRANSCRIPTION factors, *SOYBEAN, *METABOLITES, *HAPLOTYPES, *PHENYLPROPANOIDS, *PUERARIA, *LEGUMES
Abstract

Summary: Isoflavones are a class of secondary metabolites produced by legumes and play important roles in human health and plant stress tolerance. The C2H2 zinc‐finger transcription factor (TF) functions in plant stress tolerance, but little is known about its function in isoflavone regulation in soybean (Glycine max).Here, we report a C2H2 zinc‐finger TF gene, GmZFP7, which regulates isoflavone accumulation in soybean. Overexpressing GmZFP7 increased the isoflavone concentration in both transgenic hairy roots and plants. By contrast, silencing GmZFP7 expression significantly reduced isoflavone levels. Metabolomic and qRT‐PCR analysis revealed that GmZFP7 can increase the flux of the phenylpropanoid pathway. Furthermore, dual‐luciferase and electrophoretic mobility shift assays showed that GmZFP7 regulates isoflavone accumulation by influencing the expression of Isoflavone synthase 2 (GmIFS2) and Flavanone 3 β‐hydroxylase 1 (GmF3H1).In this study, we demonstrate that GmZFP7 contributes to isoflavone accumulation by regulating the expression of the gateway enzymes (GmIFS2 and GmF3H1) of competing phenylpropanoid pathway branches to direct the metabolic flux into isoflavone. A haplotype analysis indicated that important natural variations were present in GmZFP7 promoters, with P‐Hap1 and P‐Hap3 being the elite haplotypes.Our findings provide insight into how GmZFP7 regulates the phenylpropanoid pathway and enhances soybean isoflavone content. [ABSTRACT FROM AUTHOR]

Published
2023
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129. Origin, Maturity Group and Seed Coat Color Influence Carotenoid and Chlorophyll Concentrations in Soybean Seeds

Author

Gebregziabher, Berhane Sibhatu, primary, Zhang, Shengrui, additional, Ghosh, Suprio, additional, Shaibu, Abdulwahab S., additional, Azam, Muhammad, additional, Abdelghany, Ahmed M., additional, Qi, Jie, additional, Agyenim-Boateng, Kwadwo G., additional, Htway, Honey T. P., additional, Feng, Yue, additional, Ma, Caiyou, additional, Li, Yecheng, additional, Li, Jing, additional, Li, Bin, additional, Qiu, Lijuan, additional, and Sun, Junming, additional

Published
2022
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133. Natural Variation of Seed Tocopherol Composition in Diverse World Soybean Accessions from Maturity Group 0 to VI Grown in China

Author

Ghosh, Suprio, primary, Zhang, Shengrui, additional, Azam, Muhammad, additional, Gebregziabher, Berhane S., additional, Abdelghany, Ahmed M., additional, Shaibu, Abdulwahab S., additional, Qi, Jie, additional, Feng, Yue, additional, Agyenim-Boateng, Kwadwo Gyapong, additional, Liu, Yitian, additional, Feng, Huoyi, additional, Li, Yecheng, additional, Li, Jing, additional, Li, Bin, additional, and Sun, Junming, additional

Published
2022
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149. Exploring the Phenotypic Stability of Soybean Seed Compositions Using Multi-Trait Stability Index Approach

Author

Abdelghany, Ahmed M., primary, Zhang, Shengrui, additional, Azam, Muhammad, additional, Shaibu, Abdulwahab S., additional, Feng, Yue, additional, Qi, Jie, additional, Li, Jing, additional, Li, Yanfei, additional, Tian, Yu, additional, Hong, Huilong, additional, Lamlom, Sobhi F., additional, Li, Bin, additional, and Sun, Junming, additional

Published
2021
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