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| 1 | Characterization of VOC emissions from construction machinery and river ships in the Pearl River Delta of China显示文摘Speciated characterization of Volatile Organic Compounds (VOCs),including oxygenated VOCs (OVOCs),from construction machinery and river ships in China is currently lacking.In this regard,we conducted field measurement on speciated VOC (including OVOC) emissions from six construction machinery and five river ships in the Pearl River Delta (PRD) region to identify VOC emission characteristics.We noticed that OVOC emissions from construction machinery and ships accounted for more than 50%of the total VOC emissions,followed by alkenes,aromatics and alkanes.Formaldehyde and acetaldehyde were the most emission species,accounting for 61.8%-83.2%of OVOCs.For construction machinery,the fuel-based emission factors of roller,grader and pile driver were 3.12,3.12 and 7.36 g/kg,respectively.With the rigorous restraint by the national emission standards,VOC emissions of construction machinery had decreased considerably,especially during stageⅢ.Ozone formation potential was also significantly reduced due to the significant decrease in emissions of OVOCs and alkenes with higher reactivity.For river ships,the fuel-based emission factors of cargo ships and speedboat were 1.46 and 0.44 g/kg,respectively.VOC emissions from construction machinery and river ships in Guangdong Province in 2017 were 8851.0 and 4361.0 ton,respectively.This study filled the knowledge gaps of reactive gas emissions from different kinds of non-road mobile sources over the PRD,and more importantly,highlighted the necessity in adding OVOC measurement to give a complete and accurate depiction of reactive gas emissions from non-road mobile sources. | Richao Wang Zibing Yuan Junyu Zheng Cheng Li Zhijiong Huang Wenshi Li Yan Xie Yiran Wang Kaiyang Yu Lejun Duan | 2020 | Journal of Environmental Sciences2020，32，10： | 5 |
| 2 | An improved and practical route for the synthesis of enzalutamide and potential impurities study显示文摘An improved and practical synthesis of enzalutamide was accomplished in five steps.Starting from 4-bromo-2-fluoro-benzonic acid,a methyl esterification,Ullmann ligation,methyl esterification,ring closing reaction and final methyl amidation provided the target in 35% total yield with 99.8% purity.Five identified impurities were also synthesized.This efficient and economical procedure avoids the use of highly toxic reagents and multiple recrystallization operations,which is suitable for further industrialization. | Ai-Nan Zhou Bonan Li Lejun Ruan Yeting Wang Gengli Duan Jianqi Li | 2017 | Chinese Chemical Letters2017，28，2： | 2 |
| 3 | An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning显示文摘Rice panicle phenotyping is required in rice breeding for high yield and grain quality.To fully evaluate spikelet and kernel traits without threshing and hulling,using X-ray and RGB scanning,we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline.We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy(R~2 of 0.99)and speed.Faster R-CNN was also applied to indica and japonica classification and achieved 91%accuracy.The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding. | Lejun Yu Jiawei Shi Chenglong Huang Lingfeng Duan Di Wu Debao Fu Changyin Wu Lizhong Xiong Wanneng Yang Qian Liu | 2021 | The Crop Journal2021，9，1： | 1 |
| 4 | Panicle-3D: A low-cost 3D-modeling method for rice panicles based on deep learning, shape from silhouette, and supervoxel clustering显示文摘Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on 3 D panicle phenotyping has been limited. Given that existing 3 D modeling techniques do not focus on specified parts of a target object, an efficient method for panicle modeling of large numbers of rice plants is lacking. This paper presents an automatic and nondestructive method for 3 D panicle modeling. The proposed method integrates shoot rice reconstruction with shape from silhouette, 2 D panicle segmentation with a deep convolutional neural network, and 3 D panicle segmentation with ray tracing and supervoxel clustering. A multiview imaging system was built to acquire image sequences of rice canopies with an efficiency of approximately 4 min per rice plant. The execution time of panicle modeling per rice plant using 90 images was approximately 26 min. The outputs of the algorithm for a single rice plant are a shoot rice model, surface shoot rice model, panicle model, and surface panicle model, all represented by a list of spatial coordinates. The efficiency and performance were evaluated and compared with the classical structure-from-motion algorithm. The results demonstrated that the proposed method is well qualified to recover the 3 D shapes of rice panicles from multiview images and is readily adaptable to rice plants of diverse accessions and growth stages. The proposed algorithm is superior to the structure-from-motion method in terms of texture preservation and computational efficiency. The sample images and implementation of the algorithm are available online. This automatic, cost-efficient, and nondestructive method of 3 D panicle modeling may be applied to high-throughput 3 D phenotyping of large rice populations. | Dan Wu Lejun Yu Junli Ye Ruifang Zhai Lingfeng Duan Lingbo Liu Nai Wu Zedong Geng Jingbo Fu Chenglong Huang Shangbin Chen Qian Liu Wanneng Yang | 2022 | The Crop Journal2022，10，5： | 1 |
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