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| 1 | Optical observations of LIGO source GW 170817 by the Antarctic Survey Telescopes at Dome A,Antarctica显示文摘The LIGO detection of gravitational waves(GW) from merging black holes in 2015 marked the beginning of a new era in observational astronomy. The detection of an electromagnetic signal from a GW source is the critical next step to explore in detail the physics involved. The Antarctic Survey Telescopes(AST3),located at Dome A, Antarctica, is uniquely situated for rapid response time-domain astronomy with its continuous night-time coverage during the austral winter. We report optical observations of the GW source(GW 170817) in the nearby galaxy NGC 4993 using AST3. The data show a rapidly fading transient at around 1 day after the GW trigger, with the i-band magnitude declining from 17:23 ± 0:13 magnitude to 17:72 ± 0:09 magnitude in ~1:8 h. The brightness and time evolution of the optical transient associated with GW 170817 are broadly consistent with the predictions of models involving merging binary neutron stars. We infer from our data that the merging process ejected about ~10^(-2) solar mass of radioactive material at a speed of up to 30% the speed of light. | lei hu xuefeng wu igor andreoni michael c.b.ashley jeff cooke xiangqun cui fujia du zigao dai bozhong gu yi hu haiping lu xiaoyan li zhengyang li ensi liang liangduan liu bin ma zhaohui shang tianrui sun n.b.suntzeff charling tao syed a.uddin lifan wang xiaofeng wang haikun wen di xiao jin xu ji yang shihai yang xiangyan yuan hongyan zhou hui zhang jilin zhou zonghong zhu | 2017 | Science Bulletin2017，62，21： | 6 |
| 2 | Controllable Protein Adsorption and Bacterial Adhesion on Polypyrrole Nanocone Arrays显示文摘In this research, polypyrrole nanocone arrays doped with β-Naphthalene sulphonic acid(PPy-NSA) were built. This film was expected to control protein adsorption and bacterial adhesion by potential-induced reversibly redox. The scanning Kelvin probe microscopy(SKPM) and surface contact angles(SCA) tests suggested that the surface potential and wettability of PPy-NSA nanocone arrays could be controlled by simply controlling its redox property via applying potential. The controllable surface potential and wettability in return controlled the adsorption of protein and adhesion of bacteria. The proposed material might find application in the preparation of smart biomaterial surfaces that can regulate proteins and bacterial adhesion by a simple potential switching. | Zhengnan Zhou Weiping Li Tianrui He Peng Yu Guoxin Tan Chengyun Ning | 2016 | Journal of Materials Science & Technology2016，32，9： | 2 |
| 3 | Generation and optimization of slice profile data in rapid prototyping and manufacturing显示文摘 | PAN Haipeng ZHOU Tianrui | 2007 | Journal of Materials Processing Technology2007，，： | 1 |
| 4 | Generation and optimization of slice profile data in rapid prototyping and manufacturing显示文摘 | Pan Haipeng Zhou Tianrui | 2007 | Journal of Materials Processing Technology2007，，： | 1 |
| 5 | Construction and application of an ontology-based domain-specific knowledge graph for petroleum exploration and development显示文摘The massive amount and multi-sourced,multi-structured data in the upstream petroleum industry impose great challenge on data integration and smart application.Knowledge graph,as an emerging technology,can potentially provide a way to tackle the challenges associated with oil and gas big data.This paper proposes an engineering-based method that can improve upon traditional natural language processing to construct the domain knowledge graph based on a petroleum exploration and development ontology.The exploration and development knowledge graph is constructed by assembling Sinopec’s multi-sourced heterogeneous database,and millions of nodes.The two applications based on the constructed knowledge graph are developed and validated for effectiveness and advantages in providing better knowledge services for the oil and gas industry. | Xianming Tang Zhiqiang Feng Yitian Xiao Ming Wang Tianrui Ye Yujie Zhou Jin Meng Baosen Zhang Dongwei Zhang | 2023 | Geoscience Frontiers2023，14，5： | 1 |
| 6 | Generation and optimization of slice profile data in rapid prototyping and Manufacturing 显示文摘 | Pan Haipeng Zhou Tianrui | 2007 | Journal of Materials Processing Technology2007，6，12： | 1 |
| 7 | Generation and Optimization of Slice Profile Data in Rapid Prototyping and Manufactur- ing显示文摘 | PAN Haipeng ZHOU Tianrui | 2007 | Journal of Materials Processing Technology2007，，： | 1 |
| 8 | Generation and optimization of slice profile data in rapid prototyping and manufacturing显示文摘 | Pan Haipeng Zhou Tianrui | 2007 | Journal of Materials Processing Tech2007，，： | 1 |
| 9 | Generation and optimization of slice profile data in rapid prototyping and manufacturing 显示文摘 | Pan Haipeng Zhou Tianrui | 2007 | Journal of Materials Processing Technology2007，，： | 1 |
| 10 | Generation and Optimization of Slice Profile Data in Rapid Prototyping and Manufacturing显示文摘 | Pan Haipeng Zhou Tianrui | 2007 | Journal of Materials Processing Technology2007，，： | 1 |
| 11 | Soft Electronics for Health Monitoring Assisted by Machine Learning显示文摘Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed. | Yancong Qiao Jinan Luo Tianrui Cui Haidong Liu Hao Tang Yingfen Zeng Chang Liu Yuanfang Li Jinming Jian Jingzhi Wu He Tian Yi Yang Tian-Ling Ren Jianhua Zhou | 2023 | Nano-Micro Letters2023，15，5： | 1 |
| 12 | Generation and optimization of slice profile data in rapid prototyping and manufacturing显示文摘 | Pan Haipeng Zhou Tianrui | 2007 | Journal of Materials Processing Tech2007，，： | 1 |
| 13 | An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments显示文摘An accurate prediction of ship fuel consumption is critical for speed,trim,and voyage optimisation etc.While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors,research on the impact of environmental factors on fuel consumption has been lacking.In addition,although recent research efforts have widely focused on machine learning methods to predict fuel consumption,studies on hyperparameter values that are suitable for these prediction models are limited.To compensate for this deficiency in existing literature,an adaptive hyperparameter tuning method is proposed,and the effects of maritime environmental factors on fuel consumption are taken into account.Through experimentation,the proposed adaptive hyperparameter tuning method was validated via artificial neural network(ANN),support vector regression(SVR),random forest(RF),and least absolute shrink-age and selection operator(Lasso).The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination(R 2)of these algorithms.The increase of the amplitude demonstrated the following trend,in the order of the largest increase to the lowest increase:ANN,Lasso,SVM,and RF.The rates of increase were between 0.0773%and 2.1653%.Furthermore,after the environmental factors were considered,the prediction accuracies of the ANN and Lasso increased;however,the opposite was observed for the SVR and RF.As such,we confirmed that the use of Bayesian optimisation for hyper-parameter tuning can effectively improve the fuel consumption prediction accuracy,and our proposed model can therefore serve as a significant reference for calculating fuel consumption. | Tianrui Zhou Qinyou Hu Zhihui Hu Rong Zhen | 2022 | Journal of Ocean Engineering and Science2022，7，3： | 0 |
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