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| 1 | Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method显示文摘Source term identification is very important for the contaminant gas emission event. Thus, it is necessary to study the source parameter estimation method with high computation efficiency, high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However, it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO(partial swarm optimization)-Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence, a new 1-step nonlinear method combined Tikhonov regularization and PSO algorithm with nonlinear forward dispersion model was proposed. First, the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then, the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO-Tikhonov regularization method. 1-step nonlinear method even performs better than other two methods in some cases, especially for source strength and downwind distance estimation.Compared with 2-step nonlinear method, 1-step method has higher computation efficiency. On the other hand,the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally, single PSO algorithm was compared with 1-step nonlinear PSO-Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of1-step nonlinear PSO-Tikhonov regularization method is its reasonable confidence interval, which is not obtained by single PSO algorithm. Therefore, 1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term. | Denglong Ma Wei Tan Zaoxiao Zhang Jun Hu | 2018 | Chinese Journal of Chemical Engineering2018，26，2： | 3 |
| 2 | Prognostic impact of SUMO-specific protease 1 (SENP1) in prostate cancer patients undergoing radical prostatectomy显示文摘 | Tao Li Shengsong Huang Minghua Dong Yaping Gui Denglong Wu | 2012 | Urologic Oncology: Seminars and Original Investigations2012，，： | 1 |
| 3 | Evaluation of a new type of wound dressing made from recombinant spider silk protein u- sing rat models显示文摘 | Baoyong L Jian Z Denglong C | 2010 | Burns2010，36，6： | 1 |
| 4 | In vitro and in vivo research on using Antheraea pemyi silk fibroin as tissue engineering tendon scaffolds显示文摘 | Qian Fang Denglong Chen | 2009 | Materials Science and Engineering: C2009，29，5： | 1 |
| 5 | Digital economy,industrial structure,and carbon emissions:An empirical study based on a provincial panel data set from China显示文摘This paper uses the mediation effect and a spatial panel model using panel data from 30 provinces in China from 2011 to 2019 to study the relationship between the digital economy,industrial structure,and carbon emission.The research results show that the development of digital economy can effectively promote the reduction of carbon emissions.The development of the digital economy has a significant role in promoting the rationalization of the industrial structure.The digital economy not only directly suppresses carbon emissions,but also indirectly has a significant inhibitory effect on carbon emissions by promoting the rationalization and improvement of the industrial structure.The development of the digital economy suppresses the optimization of the industrial structure.The improvement of industrialization has hindered the industrialization process.It is necessary to strengthen research and development into digital technology and enhance the capacity of the digital economy to promote carbon emissions reduction. | Shuxing Chen Denglong Ding Guihong Shi Gengxuan Chen | 2022 | Chinese Journal of Population,Resources and Environment2022，20，4： | 1 |
| 6 | In vitro and in vivo research on using Antheraea pernyi silk fibroin as tissue engineering tendon scaffolds 显示文摘 | Fang Qian Chen Denglong Yang Zhiming | 2009 | Materials Science and Engineering2009，29，： | 1 |
| 7 | Preparation Technology of Nanometer Size Refractory High Density Tungsten Based Alloy Composite Powders 显示文摘 | Huang Baiyun(黄伯云) Wang Denglong (汪登龙) | 2001 | Rare Metal Materials and Engineering (稀有金属材料与工程)2001，30，6： | 1 |
| 8 | Preparation of a recombinant spider silk protein/pcl blend submicrofibrnus mat and cytocompatibility 显示文摘 | Zhao Liang Chen Denglong Wei Meihong | 2013 | Polymers & Polymer Composites2013，21，2： | 1 |
| 9 | Evaluation of a new type of wound dressing made from recombinant spider silk protein using rat models 显示文摘 | LU Baoyong ZHENG Jian CHEN Denglong | 2010 | Burns2010，36，6： | 1 |
| 10 | Overexpression of high mobility group box 1 with poor prognosis in patients after radical prostatectomy显示文摘 | Tao Li Yaping Gui Tao Yuan Guoqiang Liao Cuidong Bian Qiquan Jiang Shengsong Huang Bo Liu Denglong Wu | 2012 | BJU International . 2012 (11c)2012，，： | 1 |
| 11 | In vitro and in vivo research on using Antheraea pernyi silk fibroin as tissue engineering tendon scaffolds显示文摘 | Qian Fang Denglong Chen Zhiming Yang Min Li | 2008 | Materials Science & Engineering C2008，，5： | 1 |
| 12 | Gas leakage recognition for CO2 geological sequestration based on the time series neural network显示文摘The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO2 variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO2 data was applied to predict the daily atmospheric CO2. Further, the recurrent nonlinear autoregressive with exogenous input(NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO2 storage and transportation technologies. | Denglong Ma Jianmin Gao Zhiyong Gao Hongquan Jiang Zaoxiao Zhang Juntai Xie | 2020 | Chinese Journal of Chemical Engineering2020，28，9： | 0 |
| 13 | Preliminary clinical study on non-transecting anastomotic bulbomembranous urethroplasty显示文摘这研究试图为以后的尿道的苛评的处理调查横断得非的 anastomotic urethroplasty 的效果。有创伤的以后的尿道的苛评的 23 个病人的一个总数被注册然后把组划分了成二。在一个组, 12 个病人经历了横断得非的 anastomotic urethroplasty。在另外的组, 11 个病人经历了常规以后的尿道端对端的吻合。操作的效果用下列参数被评估:流血数量生活(QoL ) 的质量在操作,操作时间,在操作以后的 IIEF-5 分数,最大的流动率(Qmax ) ,和等级期间可伸缩。在端对端的吻合组和横断得非的 anastomotic urethroplasty 组织的常规以后的尿道之间的比较没关于平均操作时间显示出重要差别。然而,重要差别在操作期间关于流血数量在这些组之间被观察。在横断得非的 anastomotic urethroplasty 的组的病人在导管的移动以后顺利排尿。同时,从常规以后的尿道的组的一个病人端对端的吻合有困难在导管的移动以后排尿。而且,在操作的重要差别预定,为在操作,在操作以后的 IIEF-5 分数,和 QoL 的评价规模期间的数量放血被观察,而没有重要差别在操作以后在二个组的尿流动率之间被观察。总的来说,横断得非的 anastomotic urethroplasty 为以后的尿道重建是有效的,并且它能在操作以后减少可勃起的机能障碍的出现率。 | Wei Le Chao Li Jinfu Zhang Denglong Wu Bo Liu | 2017 | Frontiers of Medicine2017，11，2： | 0 |
| 14 | A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model显示文摘Gas load forecasting is important for the economic and reliable operation of the city gas transmission and distribution system.In this paper,a nonlinear autoregressive model(NARX)with exogenous inputs,support vector machine(SVM),Gaussian process regression(GPR)and ensemble tree model(ETREE)were used to predict and compare the gas load based on the gas load data in a certain region for past 3 years.The results showed that the prediction errors for most of days were higher than 10%.Further,simulation data were generated by considering the gas load variation trend,which was then combined with historical data to form the augmentation data set to train the model.The test results indicated that the prediction error of daily gas load in one year reduced to below 7%with a machine learning prediction method based on augmentation data.In addition,the model based on augmentation data set still performed better than original data in predicting the monthly gas load in last year as well as daily gas load in last month and week.Therefore,the method based on augmentation data proposed in this paper is a potentially good tool to forecast natural gas load. | Denglong Ma Ruitao Wu Zekang Li Kang Cen Jianmin Gao Zaoxiao Zhang | 2022 | Chinese Journal of Chemical Engineering2022，35，8： | 0 |
| 15 | 全基因组关联研究发现中国人群前列腺癌两个新易感位点9q31.2和19q13.4显示文摘0前言在全球范围内,前列腺癌的发病率和病死率存在着巨大差异。该病在西方发达国家发病率最高,在非裔美国人群病死率最高,而在亚洲人群中发病率及病死率均为全球最低,提示不同人种在前列腺癌的遗传方面存在异质性。在欧美和日本人群中,全基因组关联研究(GWAS)技术已经被用于检测前列腺癌的遗传易感性位点,但至今尚无关于GWAS检测中国人群前列腺癌易感位点的报道。 | Jianfeng Xu Zengnan Mo Dingwei Ye Meilin Wang Fang Liu Guangfu Jin Chuanliang Xu Xiang Wang Qiang Shao Zhiwen Chen Zhihua Tao Jun Qi Fangjian Zhou Zhong Wang Yaowen Fu Dalin He Qiang Wei Jianming Guo Denglong Wu Xin Gao Jianlin Yuan Gongxian Wang Yong Xu Guozeng Wang Haijun Yao Pei Dong Yang Jiao Mo Shen Jin Yang Jun Ou-Yang Haowen Jiang Yao Zhu Shancheng Ren Zhengdong Zhang Changjun Yin Xu Gao Bo Dai Zhibin Hu Yajun Yang Qijun Wu Hongyan Chen Peng Peng Ying Zheng Xiaodong Zheng Yongbing Xiang Jirong Long Jian Gong Rong Na Xiaoling Lin Hongjie Yu Sha Tao Junjie Feng Jishan Sun Wennuan Liu Ann Hsing Jianyu Rao Qiang Ding Fredirik Wiklund Henrik Gronberg Xiao-Ou Shu Wei Zheng Hongbing Shen Li Jin Rong Shi Daru Lu Xuejun Zhang Jielin Sun S Lilly Zheng Yinghao Sun | 2013 | 第二军医大学学报2013，34，4： | 0 |
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