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Your search keyword '"Zhang, Boyang"' showing total 727 results
Start Over Author "Zhang, Boyang" Publication Type Academic Journals
727 results on '"Zhang, Boyang"'

8. Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Author

Zhang, Boyang, Lin, Shili, Moraes, Luis, Firkins, Jeffrey, Hristov, Alexander, Kebreab, Ermias, Janssen, Peter, Bannink, André, Bayat, Alireza, Crompton, Les, Dijkstra, Jan, Eugène, Maguy, Kreuzer, Michael, McGee, Mark, Reynolds, Christopher, Schwarm, Angela, Yáñez-Ruiz, David, and Yu, Zhongtang

Subjects
Sheep, Animals, Female, Methane, Rumen, Bayes Theorem, Ruminants, Diet, Bacteria, Animal Feed, Lactation
Abstract

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lins concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Published
2023

13. Molecular mechanism and intervention measures of microvascular complications in diabetes

Author

Xu Rui, Fang Ziming, Wang Hongyu, Gu Ye, Yu Liying, Zhang Boyang, and Xu Jingyu

Subjects
microvascular complication, epidemiology, molecular mechanism, risk factors, treatment, Medicine
Abstract

In this article, the epidemiology, molecular mechanism of occurrence and development, risk factors, and treatment of diabetic microvascular complications such as diabetic nephropathy, diabetic retinopathy, and diabetic peripheral neuropathy were discussed, providing the theoretical basis for more accurate elucidation of the pathogenesis and treatment of diabetic microvascular complications.

Published
2024
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38. ECOC-based integrated learning method for fault diagnosis in nuclear power plants

Author

Sheng Guimin, Mu Yu, and Zhang Boyang

Subjects
nuclear power plant fault diagnosis, condition monitoring unit, ifwa algorithm, integrated learning, sgtr-loca, 78-02, Mathematics, QA1-939
Abstract

The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structure of the nuclear power plant system, its working mode and the association between each subsystem is carried out to analyze the monitoring parameters and fault characteristics and establish the fault data set. Secondly, an IFWA (Improved Fireworks Algorithm - Integrated Learning) algorithm is proposed to assess the severity of faults in the first circuit of a nuclear power plant. Finally, the fault diagnosis module is divided into three units according to the functional logic, i.e., condition monitoring unit, fault identification unit, and fault severity assessment unit. The results show that the diagnostic accuracy of the IFWA algorithm is 94.25% for SGTR in the single-fault diagnosis experiment and 96.25% for SGTR-LOCA in the multiple-fault diagnosis experiment. It shows that the IFWA algorithm proposed in this paper has the optimal performance capability when applied to nuclear power plant fault diagnosis and effectively assists managers in diagnosing faults and giving maintenance recommendations.

Published
2024
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