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1. Development of Lead-Based Reactor Marine Nuclear Power

Author

Wu Yican, Li Chunjing, and FDS Consortium>

Subjects
lead-based reactor, marine nuclear power, marine equipment, deep-sea nuclear power station, unmanned underwater vehicle, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Lead-based reactor nuclear power has the advantages of inherent safety, compact size, light weight, long service life, and high efficiency, and it is widely applied to advanced marine equipment, unmanned underwater vehicles, and deep-sea space stations for energy and power purposes. Conducting research on lead-based reactor marine nuclear power is key to the technological innovation of energy and power for marine equipment in China. This study clarifies the application scenarios and development demands of marine nuclear power, summarizes the development status of lead-based reactor nuclear power from the aspects of technical characteristics, representative applications, and research status, and analyzes corresponding key technologies including nuclear fuel and cladding materials, key components in the primary loop, coolant processes and oxygen control, and advanced power generation technologies. Moreover, the study explores the development challenges for lead-based reactor marine nuclear power and proposes there feasible technical routes: low-temperature lead-based reactor marine nuclear power, high-temperature and high-efficiency lead-based reactor marine nuclear power, and integrated natural-circulation lead-based reactor marine nuclear power. Furthermore, we propose the following suggestions to promote the leapfrog development of marine nuclear power in China:(1) strengthening research and development (R&D) of specialized technologies for the lead-based reactor marine nuclear power and accelerating the deployment of demonstrative projects; (2) incorporating lead-based reactors into the country's major energy strategies and creating a standards system; (3) establishing national joint R&D centers and creating new models for technological innovation and industrial development.

Published
2024
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20. Development of Lead-Based Reactor Marine Nuclear Power

Author

Wu, Yican, primary, Bai, Yunqing, primary, Li, Chunjing, primary, Li, Yang, primary, Liu, Shaojun, primary, Wang, Fang, primary, Jiang, Jieqiong, primary, Wu, Qingsheng, primary, Liu, Chao, primary, Zhou, Danna, primary, and FDS, Consortium, primary

Published
2024
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36. LncRNA IGFBP4-1 promotes tumor development by activating Janus kinase-signal transducer and activator of transcription pathway in bladder urothelial carcinoma: retraction

Author

Li, Chunjing, primary, Cao, Yu, additional, Zhang, Li, additional, Li, Jierong, additional, Wu, Huayan, additional, Ling, Fengsheng, additional, Zheng, Jintao, additional, Wang, Jianfeng, additional, Li, Bowei, additional, He, Jun, additional, Xie, Xumin, additional, Li, Zhilin, additional, Chen, Yiping, additional, He, Xuemei, additional, Guo, Mingjuan, additional, Wei, Huiling, additional, Ye, Jing, additional, Guo, Yun, additional, Zhang, Shilin, additional, Liu, Liang, additional, Liu, Guoqing, additional, and Liu, Chunxiao, additional

Published
2023
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42. Comparison of Machine Learning Methods for Predicting Soil Total Nitrogen Content Using Landsat-8, Sentinel-1, and Sentinel-2 Images.

Author

Zhang, Qingwen, Liu, Mingyue, Zhang, Yongbin, Mao, Dehua, Li, Fuping, Wu, Fenghua, Song, Jingru, Li, Xiang, Kou, Caiyao, Li, Chunjing, and Man, Weidong

Subjects
NITROGEN in soils, SYNTHETIC apertures, MACHINE learning, SYNTHETIC aperture radar, REMOTE sensing, RADARSAT satellites
Abstract

Soil total nitrogen (STN) is a crucial component of the ecosystem's nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling. [ABSTRACT FROM AUTHOR]

Published
2023
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