Academic literature on the topic 'Hunan Sheng wei yuan hui'

Graphene-based composites have received a great deal of attention in recent year because the presence of graphene can enhance the conductivity, strength of bulk materials and help create composites with superior qualities. Moreover, the incorporation of metal oxide nanoparticles such as Fe3O4 can improve the catalytic efficiency of composite material. In this work, we have synthesized a composite material with the combination of reduced graphene oxide (rGO), and Fe3O4 modified tissue-paper (mGO-PP) via a simple hydrothermal method, which improved the removal efficiency of the of methylene blue (MB) in water. MB blue is used as the model of contaminant to evaluate the catalytic efficiency of synthesized material by using a Fenton-like reaction. The obtained materials were characterized by SEM, XRD. The removal of materials with methylene blue is investigated by UV-VIS spectroscopy, and the result shows that mGO-PP composite is the potential composite for the color removed which has the removal efficiency reaching 65% in acetate buffer pH = 3 with the optimal time is 7 h. Keywords Graphene-based composite, methylene blue, Fenton-like reaction. References [1] Ma Joshi, Rue Bansal, Reng Purwar, Colour removal from textile effluents, Indian Journal of Fibre & Textile Research, 29 (2004) 239-259 http://nopr.niscair.res.in/handle/123456789/24631.[2] Kannan Nagar, Sundaram Mariappan, Kinetics and mechanism of removal of methylene blue by adsorption on various carbons-a comparative study, Dyes and pigments, 51 (2001) 25-40 https://doi.org/10.1016/S0143-7208(01)00056-0.[3] K Rastogi, J. N Sahu, B. C Meikap, M. N Biswas, Removal of methylene blue from wastewater using fly ash as an adsorbent by hydrocyclone, Journal of hazardous materials, 158 (2008) 531-540.https://doi.org/10.1016/j.jhazmat.2008.01. 105.[4] Qin Qingdong, Ma Jun, Liu Ke, Adsorption of anionic dyes on ammonium-functionalized MCM-41, Journal of Hazardous Materials, 162 (2009) 133-139 https://doi.org/10.1016/j.jhazmat. 2008.05.016.[5] Mui Muruganandham, Rps Suri, Sh Jafari, Mao Sillanpää, Lee Gang-Juan, Jaj Wu, Muo Swaminathan, Recent developments in homogeneous advanced oxidation processes for water and wastewater treatment, International Journal of Photoenergy, 2014 (2014). http://dx. doi.org/10.1155/2014/821674.[6] Herney Ramirez, Vicente Miguel , Madeira Luis Heterogeneous photo-Fenton oxidation with pillared clay-based catalysts for wastewater treatment: a review, Applied Catalysis B: Environmental, 98 (2010) 10-26 https://doi.org/ 10.1016/j.apcatb.2010.05.004.[7] Guo Rong, Jiao Tifeng, Li Ruifei, Chen Yan, Guo Wanchun, Zhang Lexin, Zhou Jingxin, Zhang Qingrui, Peng Qiuming, Sandwiched Fe3O4/carboxylate graphene oxide nanostructures constructed by layer-by-layer assembly for highly efficient and magnetically recyclable dye removal, ACS Sustainable Chemistry & Engineering, 6 (2017) 1279-1288 https://doi.org/10.1021/acssuschemeng.7b03635.[8] Sun Chao, Yang Sheng-Tao, Gao Zhenjie, Yang Shengnan, Yilihamu Ailimire, Ma Qiang, Zhao Ru-Song, Xue Fumin, Fe3O4/TiO2/reduced graphene oxide composites as highly efficient Fenton-like catalyst for the decoloration of methylene blue, Materials Chemistry and Physics, 223 (2019) 751-757 https://doi.org/ 10.1016/j.matchemphys.2018.11.056.[9] Guo Hui, Ma Xinfeng, Wang Chubei, Zhou Jianwei, Huang Jianxin, Wang Zijin, Sulfhydryl-Functionalized Reduced Graphene Oxide and Adsorption of Methylene Blue, Environmental Engineering Science, 36 (2019) 81-89 https://doi. org/10.1089/ees.2018.0157.[10] Zhao Lianqin, Yang Sheng-Tao, Feng Shicheng, Ma Qiang, Peng Xiaoling, Wu Deyi, Preparation and application of carboxylated graphene oxide sponge in dye removal, International journal of environmental research and public health, 14 (2017) 1301 https://doi.org/10.3390/ijerph14111301.[11] Yu Dandan, Wang Hua, Yang Jie, Niu Zhiqiang, Lu Huiting, Yang Yun, Cheng Liwei, Guo Lin, Dye wastewater cleanup by graphene composite paper for tailorable supercapacitors, ACS applied materials & interfaces, 9 (2017) 21298-21306 https://doi.org/10.1021/acsami.7b05318.[12] Wang Hou, Yuan Xingzhong, Wu Yan, Huang Huajun, Peng Xin, Zeng Guangming, Zhong Hua, Liang Jie, Ren MiaoMiao, Graphene-based materials: fabrication, characterization and application for the decontamination of wastewater and wastegas and hydrogen storage/generation, Advances in Colloid and Interface Science, 195 (2013) 19-40 https://doi. org/10.1016/j.cis.2013.03.009.[13] Marcano Daniela C, Kosynkin Dmitry V, Berlin Jacob M, Sinitskii Alexander, Sun Zhengzong, Slesarev Alexander, Alemany Lawrence B, Lu Wei, Tour James M, Improved synthesis of graphene oxide, ACS nano, 4 (2010) 4806-4814 https://doi.org/10.1021/nn1006368.[14] Zhang Jiali, Yang Haijun, Shen Guangxia, Cheng Ping, Zhang Jingyan, Guo Shouwu, Reduction of graphene oxide via L-ascorbic acid, Chemical Communications, 46 (2010) 1112-1114 http://doi. org/10.1039/B917705A [15] Gong Ming, Zhou Wu, Tsai Mon-Che, Zhou Jigang, Guan Mingyun, Lin Meng-Chang, Zhang Bo, Hu Yongfeng, Wang Di-Yan, Yang Jiang, Nanoscale nickel oxide/nickel heterostructures for active hydrogen evolution electrocatalysis, Nature communications, 5 (2014) 4695 https:// doi.org/10.1038/ncomms5695.[16] Wu Zhong-Shuai, Yang Shubin, Sun Yi, Parvez Khaled, Feng Xinliang, Müllen Klaus, 3D nitrogen-doped graphene aerogel-supported Fe3O4 nanoparticles as efficient electrocatalysts for the oxygen reduction reaction, Journal of the American Chemical Society, 134 (2012) 9082-9085 https://doi.org/10.1021/ja3030565.[17] Nguyen Son Truong, Nguyen Hoa Tien, Rinaldi Ali, Nguyen Nam Van, Fan Zeng, Duong Hai Minh, Morphology control and thermal stability of binderless-graphene aerogels from graphite for energy storage applications, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 414 (2012) 352-358 https://doi.org/ 10.1016/j.colsurfa.2012.08.048.[18] Deng Yang, Englehardt James D, Treatment of landfill leachate by the Fenton process, Water research, 40 (2006) 3683-3694 https://doi.org/ 10.1016/j.watres.2006.08.009.

In this paper, we propose a new method for domain adaptation in Statistical Machine Translation for low-resource domains in English-Vietnamese language. Specifically, our method only uses monolingual data to adapt the translation phrase-table, our system brings improvements over the SMT baseline system. We propose two steps to improve the quality of SMT system: (i) classify phrases on the target side of the translation phrase-table use the probability classifier model, and (ii) adapt to the phrase-table translation by recomputing the direct translation probability of phrases. Our experiments are conducted with translation direction from English to Vietnamese on two very different domains that are legal domain (out-of-domain) and general domain (in-of-domain). The English-Vietnamese parallel corpus is provided by the IWSLT 2015 organizers and the experimental results showed that our method significantly outperformed the baseline system. Our system improved on the quality of machine translation in the legal domain up to 0.9 BLEU scores over the baseline system,… Keywords: Machine Translation, Statistical Machine Translation, Domain Adaptation References [1] Philipp Koehn, Franz Josef Och, Daniel Marcu, Statistical phrase-based translation, In Proceedings of HLT-NAACL, Edmonton, Canada, 2003, 127-133. [2] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes and Jeffrey Dean, Google’s neural machine translation system: Bridging the gap between human and machine translation, CoRR, abs/1609.08144, 2016. [3] Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo and Marcello Federico, Neural versus phrase-based machine translation quality: A case study, 2016. [4] Barry Haddow, Philipp Koehn, Analysing the effect of out-of-domain data on smt systems, In Proceedings of the Seventh Workshop on Statistical Machine Translation, 2012, 422-432. [5] Boxing Chen, Roland Kuhn and George Foster, Vector space model for adaptation in statistical machine translation, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013, pp. 1285-1293. [6] Daniel Dahlmeier, Hwee Tou Ng, Siew Mei Wu4, Building a large annotated corpus of learner english: The nus corpus of learner english, In Proceedings of the NAACL Workshop on Innovative Use of NLP for Building Educational Appli-cations, 2013. [7] Eva Hasler, Phil Blunsom, Philipp Koehn and Barry Haddow, Dynamic topic adaptation for phrase-based mt, In Proceedings of the 14th Conference of the European Chapter of The Association for Computational Linguistics, 2014, pp. 328-337. [8] George Foster, Roland Kuhn, Mixture-model adaptation for smt, Proceedings of the Second Workshop on Statistical Machine Translation, Prague, Association for Computational Linguistics, 2007, pp. 128-135. [9] George Foster, Boxing Chen, Roland Kuhn, Simulating discriminative training for linear mixture adaptation in statistical machine translation, Proceedings of the MT Summit, 2013. [10] Hoang Cuong, Khalil Sima’an, and Ivan Titov, Adapting to all domains at once: Rewarding domain invariance in smt, Proceedings of the Transactions of the Association for Computational Linguistics (TACL), 2016. [11] Ryo Masumura, Taichi Asam, Takanobu Oba, Hirokazu Masataki, Sumitaka Sakauchi, and Akinori Ito, Hierarchical latent words language models for robust modeling to out-of domain tasks, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 1896-1901. [12] Chenhui Chu, Raj Dabre, and Sadao Kurohashi. An empirical comparison of simple domain adaptation methods for neural machine translation, 2017. [13] Markus Freitag, Yaser Al-Onaizan, Fast domain adaptation for neural machine translation, 2016. [14] Jia Xu, Yonggang Deng, Yuqing Gao and Hermann Ney, Domain dependent statistical machine translation, In Proceedings of the MT Summit XI, 2007, pp. 515-520. [15] Hua Wu, Haifeng Wang Chengqing Zong, Domain adaptation for statistical machine translation with domain dictionary and monolingual corpora, In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, 2008, pp. 993-1000. [16] Adam Berger, Stephen Della Pietra, and Vincent Della Pietra, A maximum entropy approach to natural language processing, Computational Linguistics, 22, 1996. [17] 18Santanu Pal, Sudip Naskar, Josef Van Genabith, Uds-sant, English-German hybrid machine translation system, In Proceedings of the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, September, Association for Computational Linguistics, 2015, pp. 152-157. [18] Louis Onrust, Antal van den Bosch, Hugo Van hamme, Improving cross-domain n-gram language modelling with skipgrams, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016, pp. 137-142. [19] Mark Aronoff, Kirsten Fudeman, What is morphology, V 8. john wiley and sons, 2011. [20] Laurence C. Thompson, The problem of the word in vietnamese, In journal of the International Linguistic Association 19(1) (1963) 39-52. https:// doi.org/1080/00437956.1963.11659787. [21] Binh N. Ngo, The Vietnamese language learning framework, Journal of Southeast Asian Language Teaching 10 (2001) 1-24. [22] Le Hong Phuong, Nguyen Thi Minh Huyen, Azim Roussanaly, Ho Tuong Vinh, A hybrid approach to word segmentation of vietnamese texts, 2008. [23] Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, Evan Herbst, Moses: Open source toolkit for statistical machine translation, In ACL-2007: Proceedings of demo and poster sessions, Prague, Czech Republic, 2007, pp.177-180. [24] Franz Josef Och, Minimum error rate training in statistical machine translation, In Proceedings of ACL, 2003, pp.160-167. [25] Andreas Stolcke, Srilm - an extensible language modeling toolkit, in proceedings of international conference on spoken language processing, 2002. [26] Papineni, Kishore, Salim Roukos, Todd Ward, WeiJing Zhu, Bleu: A method for automatic evaluation of machine translation, ACL, 2002. [27] G. Klein, Y. Kim, Y. Deng, J. Senellart, A.M. Rush, OpenNMT: Open-Source Toolkit for Neural Machine Translation. ArXiv e-prints. [28] Pratyush Banerjee, Jinhua Du, Baoli Li, Sudip Kr. Naskar, Andy Way and Josef van Genabith, Combining multi-domain statistical machine translation models using automatic classifiers, In Proceedings of AMTA 2010., 2010.