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| 1 | The enhanced X-ray Timing and Polarimetry mission—eXTP显示文摘In this paper we present the enhanced X-ray Timing and Polarimetry mission—eXTP. eXTP is a space science mission designed to study fundamental physics under extreme conditions of density, gravity and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring effects of QED, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, eXTP will be a very powerful observatory for astrophysics that will provide observations of unprecedented quality on a variety of galactic and extragalactic objects. In particular, its wide field monitoring capabilities will be highly instrumental to detect the electro-magnetic counterparts of gravitational wave sources.The paper provides a detailed description of:(1) the technological and technical aspects, and the expected performance of the instruments of the scientific payload;(2) the elements and functions of the mission, from the spacecraft to the ground segment. | ShuangNan Zhang Andrea Santangelo Marco Feroci YuPeng Xu FangJun Lu Yong Chen Hua Feng Shu Zhang Sφren Brandt Margarita Hernanz Luca Baldini Enrico Bozzo Riccardo Campana Alessandra De Rosa YongWei Dong Yuri Evangelista Vladimir Karas Norbert Meidinger Aline Meuris Kirpal Nandra Teng Pan Giovanni Pareschi Piotr Orleanski QiuShi Huang Stephane Schanne Giorgia Sironi Daniele Spiga Jiri Svoboda Gianpiero Tagliaferri Christoph Tenzer Andrea Vacchi Silvia Zane Dave Walton ZhanShan Wang Berend Winter Xin Wu Jean J.M.in't Zand Mahdi Ahangarianabhari Giovanni Ambrosi Filippo Ambrosino Marco Barbera Stefano Basso Jörg Bayer Ronaldo Bellazzini Pierluigi Bellutti Bruna Bertucci Giuseppe Bertuccio Giacomo Borghi XueLei Cao Franck Cadoux Francesco Ceraudo TianXiang Chen Yu Peng Chen Jerome Chevenez Marta Civitani Wei Cui WeiWei Cui Thomas Dauser Ettore Del Monte Sergio Di Cosimo Sebastian Diebold Victor Doroshenko Michal Dovciak YuanYuan Du Lorenzo Ducci QingMei Fan Yannick Favre Fabio Fuschino JoséLuis Ga'lvez Min Gao MingYu Ge Olivier Gevin Marco Grassi QuanYing Gu YuDong Gu DaWei Han Bin Hong Wei Hu Long Ji ShuMei Jia WeiChun Jiang Thomas Kennedy Ingo Kreykenbohm Irfan Kuvvetli Claudio Labanti Luca Latronico Gang Li MaoShun Li Xian Li Wei Li ZhengWei Li Olivier Limousin HongWei Liu XiaoJing Liu Bo Lu Tao Luo Daniele Macera Piero Malcovati Adrian Martindale Malgorzata Michalska Bin Meng Massimo Minuti Alfredo Morbidini Fabio Muleri Stephane Paltani Emanuele Perinati Antonino Picciotto Claudio Piemonte JinLu Qu Alexandre Rachevski Irina Rashevskaya Jerome Rodriguez Thomas Schanz ZhengXiang Shen LiZhi Sheng JiangBo Song LiMing Song Carmelo Sgro Liang Sun Ying Tan Phil Uttley Bo Wang DianLong Wang GuoFeng Wang Juan Wang LangPing Wang YuSa Wang Anna L.Watts XiangYang Wen Jörn Wilms ShaoLin Xiong JiaWei Yang Sheng Yang YanJi Yang Nian Yu WenDa Zhang Gianluigi Zampa Nicola Zampa Andrzej A.Zdziarski AiMei Zhang ChengMo Zhang Fan Zhang Long Zhang Tong Zhang Yi Zhang XiaoLi Zhang ZiLiang Zhang BaoSheng Zhao ShiJie Zheng Yu Peng Zhou Nicola Zorzi J.Frans Zwart | 2019 | Science China(Physics,Mechanics & Astronomy)2019，62，2： | 11 |
| 2 | The performance of MapReduce: An In-depth study 显示文摘 | Dawei J Beng C O Lei S | 2010 | Proceedings of the VLDB Endowment2010，3，12： | 1 |
| 3 | Prediction of effective thermo-mechanical properties of particulate composites显示文摘 | Ravi Annapragada S Sun Dawei Garimella Suresh V | 2007 | Computational Materials Science2007，40，2： | 1 |
| 4 | Numerical and experimental investigation of the melt casting of explosives显示文摘 | Dawei S Suresh V G | 2005 | Prop Expl Pyro2005，5，： | 1 |
| 5 | Development and dynamic modelling of a flexure-based Scott-Russell mechanism for nano-manipulation显示文摘 | YANLING T BIJAN S DAWEI Z GURSEL A | 2009 | Mechanical Systems and Signal Processing2009，23，： | 1 |
| 6 | B7-H4 as a protective shield for pancreatic islet beta cells显示文摘Auto- and alloreactive T cells are major culprits that damage β-cells in type 1 diabetes(T1D) and islet transplantation. Current immunosuppressive drugs can alleviate immune-mediated attacks on islets. T cell co-stimulation blockade has shown great promise in autoimmunity and transplantation as it solely targets activated T cells, and therefore avoids toxicity of current immunosuppressive drugs. An attractive approach is offered by the newly-identified negative T cell cosignaling molecule B7-H4 which is expressed in normal human islets, and its expression co-localizes with insulin. A concomitant decrease in B7-H4/insulin colocalization is observed in human type 1 diabetic islets. B7-H4 may play protective roles in the pancreatic islets, preserving their function and survival. In this review we outline the protective effect of B7-H4 in the contexts of T1 D, islet cell transplantation, and potentially type 2 diabetes. Current evidence offers encouraging data regarding the role of B7-H4 in reversal of autoimmune diabetes and donor-specific islet allograft tolerance. Additionally, unique expression of B7-H4 may serve as a potential biomarker for the development of T1 D. Futurestudies should continue to focus on the islet-specific effects of B7-H4 with emphasis on mechanistic pathways in order to promote B7-H4 as a potential therapy and cure for T1 D. | Annika C Sun Dawei Ou Dan S Luciani Garth L Warnock | 2014 | World Journal of Diabetes2014，5，6： | 1 |
| 7 | Neural-network-based flush air data sensing system demonstrated on a mini air vehicle显示文摘 | Ihab S Ian P Dawei G John G | | 0，，01： | 1 |
| 8 | Medical privacy protection based on granular computing显示文摘 | Wang Dawei Liau C J Hsu T S | 2004 | Artificial Intelligence in Medicine2004，32，2： | 1 |
| 9 | Development of a bio-zeolite fixed-bed bioreaetor for mitigating ammonia inhibition of anaerobic digestion with extremely high ammonium concentration livestock waste 显示文摘 | ZHENG Haiying LI Dawei STANISLAUS M S | 2015 | Chemical Engineering Journal2015，280，： | 1 |
| 10 | Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease显示文摘Alzheimer’s disease(AD)is associated with the impairment of white matter(WM)tracts.The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets[321 patients with AD,265 patients with mild cognitive impairment(MCI),279 normal controls(NC)],a unified pipeline,and independent site cross-validation.Automated fiber quantification was used to extract diffusion profiles along tracts.Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC.Machine learning models using tract-based features showed good generalizability among independent site cross-validation.The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups.We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD. | Yida Qu Pan Wang Hongxiang Yao Dawei Wang Chengyuan Song Hongwei Yang Zengqiang Zhang Pindong Chen Xiaopeng Kang Kai Du Lingzhong Fan Bo Zhou Tong Han Chunshui Yu Xi Zhang Nianming Zuo Tianzi Jiang Yuying Zhou Bing Liu Ying Han Jie Lu Yong Liu Multi-Center Alzheimer’s Disease Imaging(MCADI)Consortium | 2023 | Neuroscience Bulletin2023，39，10： | 0 |
| 11 | Artificial intelligence in dentistry:Harnessing big data to predict oral cancer survival显示文摘BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public. | Man Hung Jungweon Park Eric S Hon Jerry Bounsanga Sara Moazzami Bianca Ruiz-Negrón Dawei Wang | 2020 | World Journal of Clinical Oncology2020，11，11： | 0 |
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