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1,609 results on '"He Xiaoyan"'

1. Enhancing Agriculture QA Models Using Large Language Models

Author

He Xiaoyan

Subjects
Microbiology, QR1-502, Physiology, QP1-981, Zoology, QL1-991
Abstract

Agriculture is a complex process that requires a great deal of knowledge and experience. Yet, the complexity and often chaotic nature of web data presents a considerable challenge in obtaining suitable results. Given these production requirements, there is an urgent need to develop a model that enables machine reading comprehension and caters to automatic question-answering scenarios within the scope of agricultural production. In this paper, we construct a dataset for the experiments of document QA in agricultural scenarios. We import two model (ask-my-pdf and chat-pdf) as our baseline and use them to do the single and multiple document QA task. Then, we proposed several methods to improve the performance of the model in agriculture scenarios. At the end of the experiment, we achieved 33.3% improvement in F1 score compared to baseline and 92.8% overall answer accuracy in single document QA. For our final multiple documents QA model, we achieved 53% improvement in F1 score compared to baseline and 83.3% overall answer accuracy in the task.

Published
2024
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2. Hybrid machine learning hydrological model for flood forecast purpose

Author

Kan Guangyuan, Liang Ke, Yu Haijun, Sun Bowen, Ding Liuqian, Li Jiren, He Xiaoyan, and Shen Chengji

Subjects
hybrid machine learning, hydrological model, flood forecast, artificial neural network, k-nearest neighbor method, Geology, QE1-996.5
Abstract

Machine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems.

Published
2020
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5. Daily streamflow simulation based on the improved machine learning method

Author

Kan Guangyuan, He Xiaoyan, Ding Liuqian, Li Jiren, Hong Yang, Ren Minglei, Lei ianjie, Liang Ke, Zuo Depeng, and Huang Pengnian

Subjects
machine learning, daily streamflow simulation, hydrological model, flood forecasting, global optimization, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500
Abstract

Kan, G., He, X., Ding, L., Li, J., Hong, Y., Ren, M., Lei, T., Liang, K., Zuo, D., & Huang, P. (March-April, 2017). Daily streamflow simulation based on the improved machine learning method. Water Technology and Sciences (in Spanish), 8(2), 51-60. Daily streamflow simulation has usually been implemented by conceptual or distributed hydrological models. Nowadays, hydrological data, which can be easily obtained from automatic measuring systems, are more than enough. Therefore, machine learning turns into an effective and popular tool which is highly suited for the streamflow simulation task. In this paper, we propose an improved machine learning method referred to as PKEK model based on the previously proposed NU-PEK model for the purpose of generating daily streamflow simulation results with better accuracy and stability. Comparison results between the PKEK model and the NU-PEK model indicated that the improved model has better accuracy and stability and has a bright application prospect for daily streamflow simulation tasks.

Published
2017
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38. Identification and characterization of microRNAs in white and brown alpaca skin

Author

Tian Xue, Jiang Junbing, Fan Ruiwen, Wang Haidong, Meng Xiaolin, He Xiaoyan, He Junping, Li Hongquan, Geng Jianjun, Yu Xiuju, Song Yunfei, Zhang Danli, Yao Jianbo, Smith George W, and Dong Changsheng

Subjects
Alpaca, MicroRNAs, Deep sequencing, Coat color, Skin, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background MicroRNAs (miRNAs) are small, non-coding 21–25 nt RNA molecules that play an important role in regulating gene expression. Little is known about the expression profiles and functions of miRNAs in skin and their role in pigmentation. Alpacas have more than 22 natural coat colors, more than any other fiber producing species. To better understand the role of miRNAs in control of coat color we performed a comprehensive analysis of miRNA expression profiles in skin of white versus brown alpacas. Results Two small RNA libraries from white alpaca (WA) and brown alpaca (BA) skin were sequenced with the aid of Illumina sequencing technology. 272 and 267 conserved miRNAs were obtained from the WA and BA skin libraries, respectively. Of these conserved miRNAs, 35 and 13 were more abundant in WA and BA skin, respectively. The targets of these miRNAs were predicted and grouped based on Gene Ontology and KEGG pathway analysis. Many predicted target genes for these miRNAs are involved in the melanogenesis pathway controlling pigmentation. In addition to the conserved miRNAs, we also obtained 22 potentially novel miRNAs from the WA and BA skin libraries. Conclusion This study represents the first comprehensive survey of miRNAs expressed in skin of animals of different coat colors by deep sequencing analysis. We discovered a collection of miRNAs that are differentially expressed in WA and BA skin. The results suggest important potential functions of miRNAs in coat color regulation.

Published
2012
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