|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Specific involvement of G proteins in regulation of serum-response-factormediated gene transcription by different receptors显示文摘 | MAO JUNHAO YUAN HUIDONG XIE WEI | 1998 | J Biol Chem1998，273，27： | 1 |
| 2 | 中国科学院大连化学物理研究所的历史及中日催化界友谊回顾(英文)显示文摘This article presents a history of the origins of the Dalian Institute of Chemical Physics, Chinese Academy of Sciences (Abbreviated as DICP) and relates the recent friendship between China and Japan in the field of catalysis. Although the official beginning of DICP is in 1949 with the birth of the People's Republic of China, there are earlier roots that went back to the defeat of Russia by Imperial Japan in 1905. This began a period of occupation and exploitation of Northest Asia by Japan which did not end until its defeat by Allied forces in 1945. During the period of occupation, a laboratory was created called the Central Research Laboratory of the South Manchurian Railroad Company, which was staffed by the best and brightest researchers from Japan, and was able to develop new processes in chemicals and synthetic fuels that fed the Japanese Imperial forces. Between 1945 and 1949, Dalian was administered by the Soviet Union, and the removable equipment from the Laboratory was taken. In this period one individual stood out, the Laboratory Director Marusawa Tsuneya, who risked his life to preserve the scientific records and staff of the institute, and then stayed in Dalian for ten years after the Second World War to help China rebuild its capabilities. The Central Research Laboratory is still preserved as the Old Campus of DICP. The origin of the friendship between China and Japan in catalysis can be traced to Professor Morikawa Kiyoshi, who worked in the Central Research Laboratory and helped commercialize a shale coal liquefaction process. Upon his return to Japan he became Professor at the Tokyo Institute of Technology and in 1975 organized the first visit by Japanese academics to China. This was reciprocated in visits to Japan in 1977 and 1980 by top researchers such as Zhang Dayu, Min Enze, Tsai Khirui, Lu Jiaxi, and Lin Liwu. This resulted in numerous contacts between individuals, which multiplied geometrically. Notably among these were the personal interactions between Guo Xiexian, the Vice-Director of DICP and Tamaru Kenzi, a Professor at the University of Tokyo, which led to a friendship lasting decades and encompassing generations. In 1981, Guo Xiexian organized the first China-Japan-USA Symposium in Dalian, assisted by Tamaru Kenzi, which was attended by over 90 scientists. This meeting was a big success, and was to lead to the organization of many other multi-country conferences, further promoting exchanges. It culminated with the hosting of China of the 16th International Congress on Catalysis in 2016 in Beijing, with Li Can as President. Today DICP is a sprawling organization with over 1300 permanent staff, and is one of the powerhouses of catalysis in the world. From 2000, it has been led by Directors Bao Xinhe, Zhang Tao, and Liu Zhongmin. The Institute is making enormous contributions to research and producing cutting-edge technology, and its future is bright. | S. Ted Oyama Qin Xin Guoxing Xiong Wenjie Shen Jie Xu Hongmei Yin Youzhu Yuan Haichao Liu Huidong Zheng | 2019 | Chinese Journal of Catalysis2019，40，11： | 0 |
| 3 | Improving the WRF/urban modeling system in China by developing a national urban dataset显示文摘Accurate modeling of urban climate is essential to predict potential environmental risks in cities.Urban datasets,such as urban land use and urban canopy parameters(UCPs),are key input data for urban climate models and largely affect their performance.However,access to reliable urban datasets is a challenge,especially in fast urbanizing countries.In this study,we developed a high-resolution national urban dataset in China(NUDC)for the WRF/urban modeling system and evaluated its effect on urban climate modeling.Specifically,an optimization method based on building morphology was proposed to classify urban land use types.The key UCPs,including building height and width,street width,surface imperviousness,and anthropogenic heat flux,were calculated for both single-layer Urban Canopy Model(UCM)and multiple-layer Building Energy Parameterization(BEP).The results show that the derived morphological-based urban land use classification could better reflect the urban characteristics,compared to the socioeconomic-function-based classification.The UCPs varied largely in spatial within and across the cities.The integration of the developed urban land use and UCPs datasets significantly improved the representation of urban canopy characteristics,contributing to a more accurate modeling of near-surface air temperature,humidity,and wind in urban areas.The UCM performed better in the modeling of air temperature and humidity,while the BEP performed better in the modeling of wind speed.The newly developed NUDC can advance the study of urban climate and improve the prediction of potential urban environmental risks in China. | Huidong Li Fenghui Yuan Lidu Shen Yage Liu Zhonghua Zheng Xu Zhou | 2022 | Geoscience Frontiers2022，13，4： | 0 |
| 4 | Styrene epoxidation catalyzed by polyoxometalate/quaternary ammonium phase transfer catalysts: The effect of cation size and catalyst deactivation mechanism显示文摘Catalytic epoxidation of alkenes is an important type of organic reaction in chemical industry,and the deep insight into catalyst deactivation will help to develop new epoxidation process.In this work,series of quaternary ammoniums bearing different cationic sizes,i.e.MTOA+(methyltrioctylammonium,[(C_(8)H_(17))_(3)CH_(3)N]+),HTMA+(hexadecyltrimethylammonium,[(C_(16)H_(33))(CH_(3))_(3)N]+) and DMDOA+(dimethyldioctadecylammonium,[(C_(18)H_(37))_(2)(CH_(3))_(2)N]+) were incorporated with polyoxometalate (POM) anions to prepare phase transfer catalysts (PTCs),which were used in the styrene epoxidations.Among them,(MTOA)_(3)PW_(4)O_(24)exhibits the best catalytic performance judged from the highest styrene conversion rate(52%) and styrene oxide selectivity (93%),during which the styrene epoxidation conditions were optimized.Meanwhile,the deactivation mechanism of this kind of PTCs was proposed firstly,i.e.in the case of low H_(2)O_(2) content,the oxidant can only be used in the styrene epoxidation,in which the catalyst can transform into stable Keggin-type POM.But when the content of H_(2)O_(2) is higher,the excess H_(2)O_(2) can reactivate the Keggin-type POM into active (PW_(4)O_(24))_(3)-anions,which can trigger the ring-opening polymerization of styrene oxide.Consequently,the catalyst is deactivated by adhered poly(styrene oxide)irreversibly,which was determined by NMR spectra.In this situation,the active moiety{PO_(4)[WO(O_(2))_(2)]_(4)}_(3)-in phase-transfer catalytic system can break into some unidentified species with low W/P ratio with the presence of epoxides.This work will be beneficial for the design of new PTCs in alkene epoxidation in fine chemical industry. | Qiongna Xiao Yuyan Jiang Weiqiang Yuan Jingjing Chen Haohong Li Huidong Zheng | 2023 | Chinese Journal of Chemical Engineering2023，55，3： | 0 |
| 5 | Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model显示文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales. | Dongmei Chen Peipei Cao Lijie Yan Huidong Chen Jia Lin Xin Li Lin Yuan Kaihua Wu | 2024 | Phyton-International Journal of Experimental Botany2024，93，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员