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201 results on '"You Zhang"'

3. Technology development and cost analysis of multiple pollutant abatement for ultra-low emission coal-fired power plants in China

Author

Yongxin Zhang, Cong Luo, Yan Lu, You Zhang, Can Zhou, Zhiying Zhou, Xuecheng Wu, Chenghang Zheng, and Xiang Gao

Subjects
Air Pollutants, China, Coal, Environmental Engineering, Air Pollution, Costs and Cost Analysis, Environmental Chemistry, Industrial Development, Environmental Pollutants, General Medicine, Power Plants, General Environmental Science
Abstract

The implementation of ultra-low emission (ULE) limits (SO

Published
2023

9. High strength-ductility synergy induced by sub-rapid solidification in twin-roll cast Al–Mg–Si alloys

Author

Shao-You Zhang, Xuan Wang, Xu Liu, Yuan-Ting Mo, Cheng Wang, Tuo Cheng, Orest Ivasishin, and Hui-Yuan Wang

Subjects
Twin-roll casting, Biomaterials, Aluminum alloy, Mining engineering. Metallurgy, TN1-997, Metals and Alloys, Ceramics and Composites, Mechanical properties, Sub-rapid solidification, Microstructure, Surfaces, Coatings and Films
Abstract

Regulating the morphology and composition of Fe-containing phases can significantly improve the mechanical properties of Al–Mg–Si alloys. Here, we successfully prepared the high-performance AA6061 alloys by the route of sub-rapid solidification (SRS) based on twin-roll casting (TRC). Excellent tensile strength (∼372 MPa) and elongation (∼19%) were achieved in the peak-aged TRC sample, which increased simultaneously compared with the counterpart (∼351 MPa and ∼14%) fabricated via ingot casting (IC). It was found that the strength-ductility synergy of the TRC sample was caused by the high cooling rate, wherein the SRS can refine the microstructure significantly and promote the reaction from the AlFeSi to the AlFeMgSi (Mg:Fe:Si = 1:1.5:2.5) phase. The centerline segregation in the TRC strip could be well mitigated during solution treatment because the reduced size and Mg-incorporation accelerated the redissolution of AlFeMgSi phases. The alleviative segregation greatly promoted supersaturated solution, and thus contributing to the synergy of strength and ductility. Meanwhile, the Si-rich AlFeMgSi phase could decompose into the Fe-rich α-AlFeSi (Fe:Si > 1) during solution treatment, which expanded the solid solubility of Si and Mg. Therefore, an enhanced aging hardening response was obtained in the peak-aged TRC sample, showing the high number density and volume fraction of β” phase. The results would provide guidelines for developing high-performance Al–Mg–Si alloys based on the SRS during TRC.

Published
2022

11. Conciseness is better: Recurrent attention LSTM model for document-level sentiment analysis

Author

Jin Wang, Xuejie Zhang, and You Zhang

Subjects
Dependency (UML), Artificial neural network, Computer science, business.industry, Cognitive Neuroscience, Sentiment analysis, computer.software_genre, Computer Science Applications, Range (mathematics), Artificial Intelligence, Key (cryptography), Leverage (statistics), Artificial intelligence, business, computer, Sentence, Natural language processing, Word (computer architecture)
Abstract

Long short-term memory (LSTM) or gated recurrent units (GRUs) are usually employed to recurrently learn variable-length sentence representations with long-range dependency in document-level sentiment analysis. However, LSTM and GRUs are biased models in which the words in the tail of sentences are dominant over the words at the beginning. Although some reweighting methods, such as self-attention, are useful for reweighting each word according to its importance, when processing a document with many tokens, they tend to assign equally smaller weights to each word, leading to the keywords being covered by nonsentiment words. In this paper, a recurrent attention LSTM neural network is presented to iteratively locate an attention region covering the key sentiment words. By gradually reducing the range of attention and the number of tokens, the model can leverage the weight of the key sentiment words for final classification. Additionally, a joint loss function is implemented to highlight both keywords and appropriate attention regions. The comparative experiments are conducted on the IMDB, Yelp and Amazon document-level corpora. The results show that the proposed model outperforms several state-of-the-art methods in document-level sentiment classification.

Published
2021

12. Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

Author

Di-yao Wu, Liang Ding, Xin-you Zhang, and Meng-ling Liu

Subjects
Artificial neural network, Computer science, business.industry, Liver Neoplasms, Bayesian network, Particle swarm optimization, Bayes Theorem, Syndrome, General Medicine, Machine learning, computer.software_genre, Machine Learning, Support vector machine, Statistical classification, Test set, Fuzzy mathematics, Humans, Neural Networks, Computer, Artificial intelligence, business, computer, Extreme learning machine
Abstract

Objective By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes. Methods From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used “TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information” as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models. Results The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%. Conclusion Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Published
2021

13. Learning sentiment sentence representation with multiview attention model

Author

You Zhang, Xuejie Zhang, and Jin Wang

Subjects
Information Systems and Management, Computer science, media_common.quotation_subject, 02 engineering and technology, Machine learning, computer.software_genre, Theoretical Computer Science, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Function (engineering), Regularization (linguistics), media_common, business.industry, 05 social sciences, Sentiment analysis, Representation (systemics), 050301 education, Attention model, Expression (mathematics), Computer Science Applications, Focus (linguistics), Control and Systems Engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, 0503 education, computer, Software, Sentence
Abstract

Self-attention mechanisms in deep neural networks , such as CNN , GRU and LSTM , have been proven to be effective for sentiment analysis. However, existing attention models tend to focus on individual tokens or aspect meanings in an expression. If a text contains information on multiple sentiments from different perspectives, the existing models will fail to extract the most critical and comprehensive features of the whole text. In the present study, a multiview attention model was proposed for learning sentence representation. Instead of using a single attention, multiple view vectors were used to map the attentions from different perspectives. Then, a fusion gate was adopted to combine these multiview attentions to draw a conclusion. To ensure the differences between multiview attentions, a regularization item was introduced to add a penalty to the loss function. In addition, the proposed model can be extended to other text tasks, such as questions and topics, to provide a comprehensive representation for the classification . Comparative experiments were conducted on both multiclass and multilabel classification datasets. The results revealed that the proposed method improves the performance of several previously proposed attention models.

Published
2021

16. CFD-DEM simulations of a gas-solid-liquid fluidization system: Effects on the flow field and particle behavior of semi-dry desulfurization fluidized bed

Author

Linhang Zhu, Zhongyang Zhao, Chang Liu, You Zhang, Yongxin Zhang, Chenghang Zheng, Kun Luo, and Xiang Gao

Subjects
History, Polymers and Plastics, Process Chemistry and Technology, Chemical Engineering (miscellaneous), Business and International Management, Pollution, Waste Management and Disposal, Industrial and Manufacturing Engineering
Published
2023

19. Types of the Cambrian platform margin mound-shoal complexes and their relationship with paleogeomorphology in Gucheng area, Tarim Basin, NW China

Author

Xiangxiang He, Bin Zhang, You Zhang, Guoqi Wei, Mingyi Hu, Guangyou Zhu, Junlong Zhang, Qiang Li, Zihui Feng, and Bo Yan

Subjects
Gucheng area, Outcrop, 0211 other engineering and technologies, Energy Engineering and Power Technology, 02 engineering and technology, 010502 geochemistry & geophysics, 01 natural sciences, paleogeomorphology, Paleontology, Geochemistry and Petrology, 021108 energy, lcsh:Petroleum refining. Petroleum products, 0105 earth and related environmental sciences, geography, Tarim Basin, geography.geographical_feature_category, Front (oceanography), Shoal, Geology, Geotechnical Engineering and Engineering Geology, Petroleum reservoir, type of platform margin mound-shoal complex, lcsh:TP690-692.5, Cambrian, Facies, Economic Geology, Sedimentary rock, Progradation, Accretion (geology)
Abstract

Based on the observation of outcrops and cores, thin section identification, restoration of paleogeomorphology by residual thickness method, fine description of seismic facies, etc., the coupling relationships between the development patterns of various types of Cambrian platform margin mound-shoal complexes and paleogeomorphology in the Gucheng area of Tarim Basin have been examined. The Cambrian platform margin mound-shoal complex is divided into mound base, mound core, mound front, mound back and mound flat microfacies, which are composed of dolomites of seven textures with facies indication. The different paleogeomorphology before the deposition of mound-shoal complex in each period was reconstructed, and three types of mound-shoal complex sedimentary models corresponding to the paleogeomorphologies of four stages were established: namely, the first stage of gentle slope symmetric accretion type, the second stage of steep slope asymmetric accretion type and the third and fourth stages of steep slope asymmetric progradation type. Their microfacies are respectively characterized by “mound base - mound back + (small) mound core + mound front - mound flat” symmetric vertical accretion structure, “mound base - (large) mound core + mound front - mound flat” asymmetric vertical accretion structure, “mound base - (small) mound core + mound front - mound flat” asymmetric lateral progradation structure. With most developed favorable reservoir facies belt, the steep slope asymmetric accretion type mound-shoal complex with the characteristics of “large mound and large shoal” is the exploration target for oil and gas reservoir.

Published
2021

27. A high-performance COF-based aqueous zinc-bromine battery

Author

You Zhang, Chunlei Wei, Ming-Xue Wu, Yan Wang, Hao Jiang, Guohui Zhou, Xiao Tang, and Xiaomin Liu

Subjects
General Chemical Engineering, Environmental Chemistry, General Chemistry, Industrial and Manufacturing Engineering
Published
2023

30. Ibuprofen treatment ameliorates memory deficits in rats with collagen-induced arthritis by normalizing aberrant MAPK/NF-κB and glutamatergic pathways

Author

Nai-You Zhang, Ting-Hsuan Wang, Ching-Hsuan Chou, Kuo-Chen Wu, Chia-Ron Yang, Fan-Lu Kung, and Chun-Jung Lin

Subjects
Arthritis, Rheumatoid, Pharmacology, Memory Disorders, Excitatory Amino Acid Transporter 2, Glutamates, NF-kappa B, Animals, Cytokines, Ibuprofen, Mitogen-Activated Protein Kinases, Arthritis, Experimental, Rats
Abstract

Many studies have indicated that the risk of cognitive impairment is higher in patients with rheumatoid arthritis (RA). Additionally, patients with RA may have a lower incidence of cognitive impairment with long-term use of ibuprofen. This study was aimed at investigating the impacts of RA on memory function and the mechanisms that ibuprofen may exhibit to improve memory function in rats with collagen-induced arthritis (CIA). Ibuprofen (30 mg/kg) was given twice daily to CIA rats for two weeks starting from Day 18 following the first immunization. Memory function was measured by the Morris water maze (MWM) test and long-term potentiation (LTP). The proinflammatory cytokine levels and downstream signaling pathways, including mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-κB), were examined. Furthermore, the glutamatergic system, including glutamate transporters/receptors and brain extracellular levels of glutamate, was investigated. The results showed that the impaired learning memory in CIA rats, examined by the MWM test and LTP, can be ameliorated by ibuprofen treatment. Along with the improvement in memory deficits, ibuprofen attenuated both neuroinflammation and the associated elevated levels of phosphorylated p38, JNK, and p65 in the hippocampus of CIA rats. In addition, the decreased excitatory amino acid transporter 2 level, the increased extracellular glutamate, and the upregulated hippocampal NMDA receptor 2B of CIA rats were all normalized by ibuprofen treatment. These findings suggest that the effect of ibuprofen on the memory improvement in CIA rats is associated with the normalization of the activated MAPK and NF-κB pathways and the aberrant glutamatergic system.

Published
2022

32. Genome-Wide Identification of Zn2Cys6 Class Fungal-Specific Transcription Factors (ZnFTFs) and Functional Analysis of UvZnFTF1 in Ustilaginoidea virens

Author

Tianqiao, Song, primary, Xiong, Zhang, additional, You, Zhang, additional, Dong, Liang, additional, Jiaoling, Yan, additional, Junjie, Yu, additional, Mina, Yu, additional, Huijuan, Cao, additional, Mingli, Yong, additional, Xiayan, Pan, additional, Zhongqiang, Qi, additional, Yan, Du, additional, Rongsheng, Zhang, additional, and Yongfeng, Liu, additional

Published
2021
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33. Metacommunity ecology meets bioassessment: Assessing spatio-temporal variation in multiple facets of macroinvertebrate diversity in human-influenced large lakes

Author

Xiaolong Wang, Yongjiu Cai, Hongbin Yin, Zhixin Hu, Jani Heino, Zhijun Gong, Jianming Deng, Boqiang Qin, and You Zhang

Subjects
0106 biological sciences, Metacommunity, Ecology, Ecology (disciplines), General Decision Sciences, Species diversity, 010501 environmental sciences, Seasonality, medicine.disease, 010603 evolutionary biology, 01 natural sciences, Geography, Disturbance (ecology), medicine, Trait, Ecosystem, Species richness, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences
Abstract

Metacommunity theory emphasizes that local communities are jointly affected by environmental filtering and spatial processes. However, the roles of spatial processes are often given insufficient attention in bioassessment practices, which may bias the assessments of ecological status based on biotic metrics. Here, we quantified the relative importance and the seasonal stability of spatial processes, natural conditions and human-induced factors in structuring variation in different bioassessment metrics based on macroinvertebrate communities. Our study systems were two extensively sampled large and shallow lakes with strong nutrient gradients related to human disturbance. The roles of different drivers were examined for three kinds of indicators: general diversity, trait-based and taxonomic distinctness metrics, and their performance in characterizing human disturbance was evaluated. Overall, human-induced and spatial factors were all important in explaining variation in the three types of bioassessment metrics. Contrary to our expectations, however, we found that the importance of spatial processes on bioassessment metrics can be comparable to the effects of local environmental conditions at the within-lake scale. Furthermore, the results showed substantial seasonal variability in the relative roles of different drivers, which might be linked to life-cycle seasonality of macroinvertebrates. As expected, trait-based metrics generally were best associated with human-induced variables in both lakes, whereas general diversity and taxonomic distinctness metrics performed poorly. The low effectiveness of taxonomic distinctness metrics might due to low species richness associated with high nutrient levels. To conclude, our results suggest that bioassessment cannot exclusively rely on the idea of environmental filtering even if we focus on fine spatial scales. We hence strongly urge that spatial processes, natural drivers and temporal variability should be better considered in combination in the development and application of bioassessment approaches. In addition, taxonomic distinctness measures should be used with caution, especially for the ecosystems and organism groups typically characterized by low species richness.

Published
2019

34. A high spatial-temporal resolution emission inventory of multi-type air pollutants for Wuxi city

Author

Zengwei Yuan, You Zhang, Hui Hua, Hu Sheng, Songyan Jiang, Xuewei Liu, Ling Zhang, and Tianming Chen

Subjects
020209 energy, Strategy and Management, Population, Air pollution, 02 engineering and technology, medicine.disease_cause, Industrial and Manufacturing Engineering, chemistry.chemical_compound, 0202 electrical engineering, electronic engineering, information engineering, medicine, Emission inventory, education, Sulfur dioxide, NOx, 0505 law, General Environmental Science, Pollutant, education.field_of_study, Renewable Energy, Sustainability and the Environment, 05 social sciences, Oil refinery, Environmental engineering, Particulates, chemistry, 050501 criminology, Environmental science
Abstract

Currently, atmospheric emission inventory that includes multi-type pollutants and sources is still limited at the city level. This study developed a high-resolution emission inventory of eight pollutants based on detailed investigated data for Wuxi city in 2015. Results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), total suspended particulates (TSP), particulate matter with an aerodynamic diameter of 10 μm or less (PM10), particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5), carbon monoxide (CO), ammonia (NH3), and volatile organic compounds (VOCs) were 36.9 kt, 95.2 kt, 287.3 kt, 127.8 kt, 46.0 kt, 626.4 kt, 10.9 kt, and 104.9 kt, respectively. Power plants were the major contributors of SO2 and NOx, accounting for 34.6% and 36.3% of the total emissions, respectively. Road dust was the dominant source of particulate matter, contributing 63.1%, 57.8% and 38.2% to the total TSP, PM10 and PM2.5 emissions, respectively. Around 67.0% of CO emissions were from iron and steel plants, while 50.1% of NH3 emissions were from livestock and poultry sectors. VOCs emissions largely came from oil refinery and chemical industry, with a share of 41.3%. High NH3 emissions were found in the southwest and northeast of Wuxi where high-intensity agriculture was located, while other pollutants were mainly concentrated on the eastern Wuxi where heavy industries, crowded traffic and highly dense population were located. Most pollutants from the industrial source exhibited the lowest value in February due to the Chinese Spring Festival with the exception of CO and NH3. This study helps to formulate the targeted air pollution control measures for Wuxi city by providing detailed information regarding the spatial-temporal distribution of multi-type air pollutants.

Published
2019

35. Observation of electroweak W±Z boson pair production in association with two jets in pp collisions at s=13 TeV with the ATLAS detector

Author

Benjamin Jacque Gutelman, H. Zhang, William Mickael Barbe, Xiang Li, Polina Nechaeva, Mats Joakim Robert Olsson, A. Borisov, Mark Stockton, Yoichi Ikegami, G. Di Gregorio, Daniel Andreas Britzger, Marco Battaglia, Tomas Sykora, L. La Rotonda, Mauro Citterio, Valery Schegelsky, Sabrina Groh, Jorge Andres Lopez Lopez, Massimo Lazzaroni, Simen Hellesund, Bogdan Malaescu, Oldrich Kepka, Alessandra Baas, Armin Michael Nairz, Marzieh Bahmani, Kyungeon Choi, Sebastian Heer, Ondrej Hladik, Tatsumi Koi, Tomas Jakoubek, Krisztian Peters, Laurent Vacavant, Giancarlo Spigo, Kathleen Whalen, Valentinos Christodoulou, T. R. V. Billoud, F. Zhang, Vladimir Zhulanov, Athina Kourkoumeli-Charalampidi, C-Q. Li, Michel Lefebvre, Elliot Reynolds, Edson Carquin, Gabriele Chiodini, Pavel Murin, S. Willocq, Matthew Glenn Kurth, Marco Fraternali, Thibault Guillemin, P. S. Wells, Alexander Melzer, Alan James Taylor, Philip Phillips, Samuel Silverstein, E. Celebi, Chav Chhiv Chau, Philipp Thomas Koenig, Hajime Nanjo, H. H. Williams, Giada Mancini, M. A. Pleier, Qiang Li, Guillermo Nicolas Hamity, Alessandro Calandri, Vincent Croft, Emmerich Kneringer, Donatella Cavalli, U. Mallik, Tamar Zakareishvili, Wendy Taylor, C. A. Lee, Emmanuel Sauvan, Lixin Ma, P. Gadow, Sergey Karpov, Ralf Hertenberger, G. Lehmann Miotto, C. M. Vergel Infante, Nedaa Asbah, Kerim Suruliz, Monika Wielers, Adam Edward Barton, Li Zhou, Eric Lancon, Mohamed Ouchrif, Christopher Willis, Irakli Minashvili, Gabriella Gaudio, Cecilia Tosciri, R. Zou, S. De Castro, Thomas Meideck, Yiming Abulaiti, Joseph Haley, F. Wang, Y. Amaral Coutinho, Y. W. Liu, Vadim Gratchev, Tatiana Lyubushkina, Xuai Zhuang, Samira Hassani, C. Da Via, J. Llorente Merino, Umar Gul, Ian Brock, R. Kowalewski, Lukasz Zwalinski, Andreas Reiss, Masahiro Morinaga, Terry Richard Wyatt, A. T. Watson, Nurcan Ozturk, Stephen Albert Weber, Magdalena Slawinska, Huajie Cheng, Shaojun Sun, D. Golubkov, Jared David Little, Oleg Bulekov, M. Cano Bret, Thomas Trefzger, Daniel Scheirich, Runyu Bi, Daniel Bullock, Ole Røhne, Sheena Schier, D. Camarero Munoz, Peter Kodys, Corentin Allaire, Michal Simon, Steinar Stapnes, Ludovico Pontecorvo, Harish Potti, Yee Chinn Yap, Garabed Halladjian, Klaus Hamacher, Haiping Peng, Bartosz Sebastian Dziedzic, Lorenzo Massa, Christoph Amelung, F. G. Oakham, Josh Moss, Enrique Kajomovitz, M. V. Silva Oliveira, Evgenii Baldin, Louis Helary, Anna Mastroberardino, Bruno Lenzi, David Kirchmeier, Silvia Franchino, A. De Santo, Carsten Dulsen, Stephen Hillier, M.A. Haleem, Luis Hervas, Bjarne Stugu, Venugopal Ellajosyula, D. Boscherini, Miaoran Lu, Knut Oddvar Hoie Vadla, Andrea Sansoni, Frank Siegert, Judith Katzy, Silvia Biondi, Alessia Murrone, Peter Buchholz, Martine Bosman, Sergio Gonzalez-Sevilla, Tatiana Klioutchnikova, Michael Pitt, Jovan Mitrevski, K. F. Di Petrillo, J. L. La Rosa Navarro, Fabrice Balli, Zdenek Dolezal, Mark Sutton, José Salt, Danuta Kisielewska, M. Vicente Barreto Pinto, Carlo Schiavi, Anna Macchiolo, Philipp Mogg, Julia Isabell Djuvsland, Matteo Bedognetti, Matthew Peter Heath, G. A. Chelkov, Abdeslam Hoummada, Laura Jeanty, Yaquan Fang, Patrick Rieck, Benjamin Bordy Tannenwald, Paolo Iengo, Rachel Rosten, E. L. Barberio, Katharina Bianca Jakobi, Max Fredrik Isacson, Judita Mamuzic, Eitan Gozani, Fabrizio Petrucci, P. H. Sales De Bruin, Matthew Berg Epland, Giulia Gonella, Jelena Jovicevic, Janet Dietrich, Lesya Anna Horyn, Tom McCarthy, James Shank, Christian Weiser, Seyed Ruhollah Shojaii, Barbara Liberti, Daniele Madaffari, Uladzimir Kruchonak, Cristian Stanescu, Ovsat Abdinov, Kirill Prokofiev, N. De Groot, Kristian Gregersen, Tobias Bisanz, Tadej Novak, Remco Castelijn, D. P. C. Sankey, Bojan Hiti, Maximilian Hils, Alexey Buzykaev, D. Rousseau, Bo Li, Miha Zgubic, Thomas Philippe Calvet, Yicheng Guo, O. Le Dortz, Liang Li, Francois Vazeille, Ilias Panagoulias, Daniel Marley, Christophe Raymond Goudet, Tord Ekelof, Monica Verducci, L. Zhang, R. Cherkaoui El Moursli, Ruo-yu Shang, Shunichi Akatsuka, Attila Krasznahorkay, Ismet Siral, Dimitrios Iliadis, Cherifa Sabrina Amrouche, Fabio Cardillo, Antonio Ereditato, Colleen Jennifer Treado, F. A. Jimenez Morales, Harinder Singh Bawa, Nathan Allen Grieser, Tina Sfiligoj, Saverio D'Auria, Oliver Stelzer-Chilton, Oliver James Winston, Petter Nilsson, Rachel Maria Avramidou, Yongsun Kim, Andrew D. Hamilton, Arthur James Horton, T. Cuhadar Donszelmann, R. S. Orr, Thomas Alfons Beermann, Daniel Turgeman, Marija Marjanovic, Nikolai Rusakovich, Theodor Christian Herwig, Hongfang Liu, Margherita Primavera, Gabija Zemaityte, Gonzalo Enrique Orellana, Pawel Klimek, Nikola Makovec, Daniel Lawrence Briglin, Dave Charlton, Rachel Hyneman, Pedro Urrejola, Hulin Wang, Igor Gavrilenko, Matteo Bauce, Adam Jinaru, S. V. Zeißner, H. S. Chen, Antonio Limosani, S. H. Robertson, Harshna Jivan, W. Buttinger, C. J. S. Young, Zihao Jiang, Fabrizio Miano, Y. Zhao, Duc Ta, Raymond Brock, Ahmet Bingul, Elzbieta Richter-Was, Meghan Frate, Ryan White, Michal Marcisovsky, Yoav Afik, Adam James Bozson, Martin Aleksa, Giovanni Crosetti, Stefania Antonia Stucci, Andrew Stuart Bell, Patrick Bryant, Giovanna Bruni, Klaus Mönig, Flera Rizatdinova, Sabine Crépé-Renaudin, Stefania Spagnolo, Marzio Nessi, Filomena Sopkova, William Joseph Johnson, Lewis Joseph Wilkins, L. Cerda Alberich, J. G. Panduro Vazquez, Ph. Schune, W-M. Yao, Valerie Susanne Lang, Nikola Lazar Whallon, Masato Aoki, P. A. Delsart, Nicoletta Garelli, Francesco Rubbo, Michael R. Wessels, D. Chakraborty, German D Carrillo-Montoya, Oliver Kortner, Rachael Ann Creager, Paul Jackson, Ozan Arslan, John Derek Chapman, Xanthe Hoad, W. Kozanecki, S. R. Paredes Saenz, Andreas Sogaard, Marcella Capua, Oliver Ricken, Katharina Becker, Marta Lidia Rimoldi, Regina Moles-Valls, L. Manhaes de Andrade Filho, E. Sideras Haddad, Wade Cameron Fisher, David Reikher, Katharina Bierwagen, Jeremy Love, Yuta Sano, Hans Krüger, K. P. Mistry, Nicolas Maximilian Köhler, Kendall Reeves, Daniel Muenstermann, Michael Schernau, Claus Gössling, Benedetto Gorini, Olivier Arnaez, Antonia Strubig, Kevin Varvell, Djamel Eddine Boumediene, Simon Berlendis, Elliot Lipeles, Saleh Sultansoy, Antonio Salvucci, R. Tanaka, Brigitte Vachon, Arthur Eugen Bolz, Noam Hod, Jiancong Zeng, Alexander Held, Thomas Maier, Pavel Tsiareshka, Olga Igonkina, Sarah Jones, Victor Maleev, Karl Jakobs, Jan-Ulf Mjörnmark, James Boyd, G. Mc Goldrick, Frederic Deliot, Tancredi Carli, Roberto Cardarelli, Alessandra Betti, Andrew Geoffrey Foster, Mateusz Dyndal, Serena Palazzo, G. Polesello, Rebecca Hayley Pickles, Archil Durglishvili, N. Kimura, Ralf P. Richter, Y. F. Ryabov, M. Silva, M. H. Klein, Stephen Burke, Stéphane Viel, C. Dallapiccola, Johannes Erdmann, Emily McDonald, Ph. Schwemling, Giuliano Gustavino, Ruchika Nayyar, Ulrike Blumenschein, Sergey Senkin, A. Onofre, Oleh Kivernyk, Jemal Khubua, Stephen Watts, Franz E. Bauer, I. Luise, Ehud Duchovni, James Walder, Robert Leone, Petar Bokan, Silvia Resconi, Mazuza Ghneimat, Tim Adye, Verena Maria Walbrecht, R. D. Kass, Zdenek Hubacek, Giorgio Chiarelli, Ana Cueto, L. Barranco Navarro, Y. Huang, Katsuo Tokushuku, Sylvain Blunier, Tadashi Maeno, R. M. D. Carney, Jens Janssen, Pavel Staroba, Minyu Feng, Marta Maria Perego, Matthew James Sullivan, Mario Sannino, Jaroslav Guenther, Bruno Galhardo, Antonio Giannini, Carlo Enrico Pandini, Joseph Reichert, Markus Schumacher, Mahmoud Alstaty, Nicola Abraham, J. Machado Miguens, Philipp Horn, Eric Drechsler, Leigh Schaefer, Mariel Nelson Pettee, Giorgi Arabidze, D. Shoaleh Saadi, Anjishnu Bandyopadhyay, Thilo Pauly, Sarah Demers, A. Seiden, Matthias Saimpert, James Catmore, Artur Trofymov, Hideki Okawa, Soumya Mohapatra, Gerhard Brandt, Stylianos Angelidakis, Janusz Chwastowski, Sharka Todorova-Nova, Fumiya Yamane, T. Nguyen Manh, Stéphane Jézéquel, S. E. von Buddenbrock, Christian Grefe, Francesco Cirotto, Nicola Semprini-Cesari, Robert Johannes Langenberg, Sven Menke, Mogens Dam, Zuzana Barnovska-Blenessy, Thomas Daubney, L. Di Ciaccio, Mohammad Jawad Kareem, Jan Schaeffer, Evgeniya Cheremushkina, Stewart Patrick Swift, S. Chen, Remi Lafaye, Laurent Serin, Antonio Zoccoli, Jahred Adelman, Alain Bellerive, Takashi Mitani, Anna Lipniacka, Christian Ohm, Alice Polyxeni Morris, Kalliopi Iordanidou, Xiaoguang Yue, H. Hakobyan, Gregor Kramberger, Ewelina Lobodzinska, H. B. Liu, Shaun Roe, X. Y. Zhang, Olga Rezanova, Samuel Calvet, Tairan Xu, Simonetta Gentile, Igor Aleksandrov, J. B. 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Cakir, Chris Sander, Anna Shcherbakova, Evelin Meoni, Lorenzo Rossini, Ivan Angelozzi, Stefan Stonjek, Mark Hodgkinson, D. M. Portillo Quintero, Alan Peyaud, Magda Anna Chelstowska, Simone Sottocornola, Enrico Junior Schioppa, Henso Abreu, R. E. Long, Q. Ouyang, Evangelos Leonidas Gkougkousis, Pierre Marc Luzi, Sascha Mehlhase, Sergii Raspopov, Yanyan Du, Cyril Becot, Aviv Ruben Cukierman, Cunfeng Feng, Yasushi Nagasaka, R. J. Hawkings, Dave Lewis, Ph Gris, Matt Zhang, I. I. Tsukerman, P. Johansson, William Trischuk, Helge Christoph Beck, Oxana Smirnova, Konstantinos Bachas, R. D. Schamberger, Pascal Pralavorio, Lamberto Luminari, Maciej Trzebinski, Ji Zhu, Stuart Derek Walker, Gabriella Sciolla, Victor Solovyev, Benoit Lefebvre, Thorwald Klapdor-kleingrothaus, Savanna Marie Shaw, J. A. Wilson, M. De Santis, C. A. Solans Sanchez, Tomas Slavicek, J. C. Rivera Vergara, W. Wang, Per Olov Joakim Gradin, J. 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Subjects
Physics, Nuclear and High Energy Physics, Particle physics, Muon, Luminosity (scattering theory), Large Hadron Collider, 010308 nuclear & particles physics, High Energy Physics::Phenomenology, Electroweak interaction, 7. Clean energy, 01 natural sciences, Pair production, medicine.anatomical_structure, Atlas (anatomy), 0103 physical sciences, medicine, High Energy Physics::Experiment, 010306 general physics, Lepton, Boson
Abstract

An observation of electroweak W±Z production in association with two jets in proton–proton collisions is presented. The data collected by the ATLAS detector at the Large Hadron Collider in 2015 and 2016 at a centre-of-mass energy of s=13 TeV are used, corresponding to an integrated luminosity of 36.1fb−1 . Events containing three identified leptons, either electrons or muons, and two jets are selected. The electroweak production of W±Z bosons in association with two jets is measured with an observed significance of 5.3 standard deviations. A fiducial cross-section for electroweak production including interference effects and for a single leptonic decay mode is measured to be σWZjj−EW=0.57−0.13+0.14(stat.)−0.06+0.07(syst.)fb . Total and differential fiducial cross-sections of the sum of W±Zjj electroweak and strong productions for several kinematic observables are also measured.

Published
2019

36. Effect of graphene oxide additive on tribocorrosion behavior of MAO coatings prepared on Ti6Al4V alloy

Author

Chen Xiaoyi, You Zhang, Zhao Zicong, Fei Chen, Zuo You, Peihang Yu, and Li Tianlu

Subjects
Materials science, Tribocorrosion, Oxide, General Physics and Astronomy, chemistry.chemical_element, 02 engineering and technology, engineering.material, 010402 general chemistry, 01 natural sciences, law.invention, Corrosion, chemistry.chemical_compound, Coating, law, Composite material, Polarization (electrochemistry), Graphene, Surfaces and Interfaces, General Chemistry, 021001 nanoscience & nanotechnology, Condensed Matter Physics, 0104 chemical sciences, Surfaces, Coatings and Films, Dielectric spectroscopy, chemistry, engineering, 0210 nano-technology, Titanium
Abstract

Titanium and its alloys possess the superiority on corrosion resistance and the shortage on wear resistance. Tribocorrosion, a new study orientation concerning both corrosion and wear resistances and fascinating researchers in recent years, has to be considered on the titanium, its alloys and their surface treated materials. In this study, graphene oxide (GO) particles inserted coatings were prepared by micro-arc oxidation (MAO) treatment with various GO concentrations. The composition, morphology and topography were measured to analyze the structure and properties of coatings. Electrochemical impedance spectroscopy (EIS) and polarization measurements were utilized to assess the corrosion resistance of samples. Tribocorrosion behavior was evaluated by the open circuit potential (OCP) and coefficient of friction (COF) evolutions in advance to, during and after reciprocating sliding. The results showed that in GO concentration of 5 mL/L and 10 mL/L, the GO particles were inserted into the MAO coatings and the MAO coating by adding 10 mL/L GO particles possessed best tribocorrosion resistance.

Published
2019

37. Exosomes of bone-marrow stromal cells inhibit cardiomyocyte apoptosis under ischemic and hypoxic conditions via miR-486-5p targeting the PTEN/PI3K/AKT signaling pathway

Author

You Zhang, Xu Wang, Xiang-Hua Sun, and Jie Hui

Subjects
Male, Stromal cell, Cell, Apoptosis, Myocardial Reperfusion Injury, 030204 cardiovascular system & hematology, Exosomes, Exosome, Rats, Sprague-Dawley, Phosphatidylinositol 3-Kinases, 03 medical and health sciences, 0302 clinical medicine, medicine, Animals, PTEN, Myocytes, Cardiac, Viability assay, Cells, Cultured, PI3K/AKT/mTOR pathway, biology, Chemistry, Akt/PKB signaling pathway, PTEN Phosphohydrolase, Mesenchymal Stem Cells, Hematology, Cell biology, MicroRNAs, medicine.anatomical_structure, Gene Expression Regulation, 030220 oncology & carcinogenesis, biology.protein, Proto-Oncogene Proteins c-akt, Signal Transduction
Abstract

Background Myocardial ischemia–reperfusion injury (MIRI) is a major obstacle in the treatment of ischemic heart disease. Recent studies have shown that exosomes—small membrane vesicles secreted by most cell types—could have a protective effect on the ischemic myocardium. In this study we explored the effect of exosomes derived from bone-marrow stromal cells (BMSC-exo) on cardiomyocyte apoptosis and MIRI. Methods Exosomes were purified from culture media using the ExoQuick kit and observed using transmission electron microscopy. Cell viability was assessed by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Cell apoptosis was analyzed by flow cytometry using the Annexin-V/PI stain. The expression levels of microRNA (miRNA), messenger RNA (mRNA) and PTEN/PI3K/AKT-pathway-related proteins were detected by qRT-PCR and western blot, respectively. Myocardial ischemia was simulated by incubating H9C2 cells in a hypoxia/reoxygenation (H/R) conditioned rat MIRI model. Results BMSC-exo induced the proliferation of H9C2 cells and rescued H9C2 cells from apoptosis in the H/R model, indicating that BMSC-exo has a protective effect on cardiomyocyte injury caused by H/R. Using transgenic H9C2 cells, we found that miR-486-5p in BMSC-exo suppressed the H/R-triggered apoptosis of H9C2 cells. In addition, BMSC-exo repressed the expression of PTEN in H9C2 cells via miR-486-5p, and subsequently activated the PI3K/AKT pathway in vitro. Moreover, the myocardial injury caused by ischemia/reperfusion was repaired by BMSC-exo which activates the PI3K/AKT pathway via miR-486-5p in vivo. Conclusion Our results suggested that exosomes from BMSCs have a protective effect on myocardium ischemic injury. MiR-486-5p carried by BMSC-exo plays a pivotal role in the regulatory process by suppressing PTEN expression, activating the PI3K/AKT signaling pathway, and subsequently inhibiting the apoptosis of injured cardiomyocytes.

Published
2019

38. Influence of graphene oxide on the antiwear and antifriction performance of MAO coating fabricated on Mg Li alloy

Author

Fei Chen, Cuiwei Du, Zhiyong Liu, You Zhang, Yulin Zhang, and Xiaofei Wang

Subjects
Materials science, Alloy, Oxide, 02 engineering and technology, engineering.material, 010402 general chemistry, 01 natural sciences, Frictional coefficient, law.invention, chemistry.chemical_compound, Coating, law, Materials Chemistry, Ceramic, Composite material, Graphene, Abrasive, Surfaces and Interfaces, General Chemistry, 021001 nanoscience & nanotechnology, Condensed Matter Physics, 0104 chemical sciences, Surfaces, Coatings and Films, chemistry, visual_art, visual_art.visual_art_medium, engineering, Adhesive, 0210 nano-technology
Abstract

Ceramic coatings with incorporated graphene oxide (GO) sheets were prepared by a micro-arc oxidation technique to improve the antiwear and antifriction performance of the Mg Li alloy. The GO-containing coating is mainly composed of MgO, Mg2SiO4 and SiO2 phases and possesses higher compactness, thickness and hardness than the GO-free coating. Furthermore, the GO-containing coating is capable of sustaining a convenient, low, constant frictional coefficient and is accompanied by slight adhesive and abrasive wear during the whole wear testing. Although the wear depth and volumes of all the specimens increased as the friction temperature increased to 200 °C, the wear volumes of the GO-containing coating were reduced by 80.60% and 50.08% when compared with the Mg Li alloy and GO-free coating, respectively.

Published
2019

39. 4D liver tumor localization using cone-beam projections and a biomechanical model

Author

David Parsons, Pam Lee, Joubin Nasehi Tehrani, Xun Jia, You Zhang, Jing Wang, Tsuicheng Chiu, Jing Cai, Jeffrey J Meyer, Bin Li, Michael R. Folkert, and Xiaokun Huang

Subjects
Liver tumor, Computer science, Cbct image, Article, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, stomatognathic system, Image Processing, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, Four-Dimensional Computed Tomography, Phantoms, Imaging, business.industry, Radiotherapy Planning, Computer-Assisted, Liver Neoplasms, Hematology, Deformation vector, Cone-Beam Computed Tomography, medicine.disease, Finite element method, Hausdorff distance, Oncology, 030220 oncology & carcinogenesis, Digitally reconstructed radiographs, Biomechanical model, Artificial intelligence, business, Algorithms
Abstract

Purpose To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. Methods/Materials To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step’s accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est’s accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT ‘reference’ images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. Results Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from −0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. Conclusions Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

Published
2019

40. Automatic generation of benchmarks for I/O-intensive parallel applications

Author

Weizhe Zhang, Meng Hao, Marc Snir, Laurence T. Yang, and You Zhang

Subjects
Input/output, Computer Networks and Communications, Computer science, Computation, 020206 networking & telecommunications, 02 engineering and technology, Parallel computing, Execution time, Theoretical Computer Science, Artificial Intelligence, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Performance prediction, Benchmark (computing), Overhead (computing), 020201 artificial intelligence & image processing, Software, TRACE (psycholinguistics)
Abstract

The benchmarks of I/O-intensive parallel applications are important for evaluating and optimizing HPC softwares and hardwares. However, extracting high-fidelity benchmarks which can fully reflect computation, communication, and I/O behaviors of original I/O-intensive parallel applications is very difficult. This work contributes a framework which can automatically generate benchmarks for I/O-intensive parallel applications. We demonstrate our framework on Taub and TianHe-2 supercomputers with five NAS Parallel Benchmarks (NPB) and four I/O-intensive parallel applications. The results show that our trace merging algorithm and trace compressing algorithm are better than others, and the generated benchmarks can accurately mimic the computation, communication, and I/O behaviors of original I/O-intensive parallel applications. Also, these generated benchmarks can be used to predict the performance of original applications, while reducing the prediction overhead by scaling down the execution time of benchmark proportionally.

Published
2019

41. Insulin-induced gene 1 (INSIG1) inhibits HIV-1 production by degrading Gag via activity of the ubiquitin ligase TRC8

Author

Xinqi Liu, Jing Lu, Jing Ma, and You Zhang

Subjects
0301 basic medicine, THP-1 Cells, Endosome, Ubiquitin-Protein Ligases, Receptors, Cell Surface, Protein degradation, Virus Replication, gag Gene Products, Human Immunodeficiency Virus, Biochemistry, Jurkat Cells, 03 medical and health sciences, Lysosome, medicine, Humans, Molecular Biology, chemistry.chemical_classification, DNA ligase, 030102 biochemistry & molecular biology, biology, Chemistry, Endoplasmic reticulum, Intracellular Signaling Peptides and Proteins, Membrane Proteins, Cell Biology, Sterol regulatory element-binding protein, Ubiquitin ligase, Cell biology, HEK293 Cells, 030104 developmental biology, medicine.anatomical_structure, Proteasome, Proteolysis, HIV-1, biology.protein, Lysosomes, HeLa Cells
Abstract

Insulin-induced gene 1 (INSIG1) regulates sterol synthesis by mediating the activation of sterol regulatory element–binding protein (SREBP) and the degradation of the HMG-CoA reductase (HMGCR). INSIG1 is up-regulated during HIV-1 infection, but its role in HIV-1 infection is unknown. In this report, using pseudovirus production, protein overexpression, and gene knockouts, we found that INSIG1 inhibits HIV-1 production by accelerating the degradation of the HIV-1 Gag protein. Unlike the degradation of HMGCR via the E3 ubiquitin ligase autocrine motility factor receptor (AMFR), a process that depends on the proteasome, INSIG1 coordinated with another ligase, translocation in renal carcinoma chromosome 8 (TRC8), and promoted Gag degradation through the lysosome pathway. We conclude that INSIG1 functions as a sentinel responsive to HIV-1 production and inhibits HIV-1 replication by degrading Gag, a process occurring at intracellular membrane sites such as the endoplasmic reticulum and endosomes where both INSIG1 and Gag may be located.

Published
2019

42. Intensive carbon dioxide emission of coal chemical industry in China

Author

Zengwei Yuan, Songyan Jiang, Hui Hua, Xuewei Liu, Cécile Bulle, Manuele Margni, and You Zhang

Subjects
business.industry, 020209 energy, Mechanical Engineering, Environmental engineering, chemistry.chemical_element, 02 engineering and technology, Building and Construction, Management, Monitoring, Policy and Law, Coal liquefaction, chemistry.chemical_compound, General Energy, 020401 chemical engineering, chemistry, Carbon dioxide, 0202 electrical engineering, electronic engineering, information engineering, Environmental science, Energy transformation, Coal gasification, Coal, Scenario analysis, 0204 chemical engineering, Tonne, business, Carbon
Abstract

As the largest producer of coal chemical products in the world, China faces tremendous pressure to reduce its carbon emission. An accurate quantification of the carbon dioxide (CO2) emission of coal chemical industry in China is therefore necessary. However, due to the variety of coal chemical products and limitations of CO2 emission factors, the total CO2 emission of coal chemical industry has yet to be determined. In this study, local CO2 emission factors of coal chemical products in China are published based on first hand data from twenty-three coal chemical enterprises and the total CO2 emission of China's coal chemical industry is extrapolated. The provincial-level spatial distribution of the CO2 emission of coal chemical industry is presented to assist the government in identifying key emission reduction areas. Additionally, scenario analysis of CO2 emission for China’s modern coal chemical industry in 2020 is conducted to determine whether the development of the modern coal chemical industry will have a significant impact on future CO2 emission, as well as the effect of carbon capture, utilization and storage technologies on the reduction in carbon emission. The estimate shows that the total CO2 emission of the coal chemical industry in 2015 was 607 million tonnes (Mt), accounting for approximately 5.71% of China’s total CO2 emission. The figure is higher than the total annual CO2 emission of a country such as Canada (555 Mt) or Brazil (486 Mt). Quantifying the emission of the coal chemical industry is therefore critical to understand the global carbon budget. The spatial distribution shows that Shandong, Inner Mongolia and Shanxi release one-third of the coal chemical industry’s total CO2 emission. Considering the development of the modern coal chemical industry, its CO2 emission is predicted to be as high as 416.52 million tonnes in 2020. However, the CO2 emission could be reduced by 317.98 million tonnes when carbon capture, utilization and storage are applied to process and energy systems simultaneously. This paper quantifies the CO2 emission of the coal chemical industry in China for the first time, identifies key chemical products and the provinces in which they are produced, explores the carbon reduction potential by scenario analysis, and provides specific data to support the assessment of effective CO2 reduction policy.

Published
2019

43. Phosphorus footprint in China over the 1961–2050 period: Historical perspective and future prospect

Author

You Zhang, Songyan Jiang, Helen P. Jarvie, Ling Zhang, Xuewei Liu, Hu Sheng, Zengwei Yuan, Xin Liu, and Hui Hua

Subjects
China, Environmental Engineering, 010504 meteorology & atmospheric sciences, Natural resource economics, Population, chemistry.chemical_element, 010501 environmental sciences, History, 21st Century, 01 natural sciences, Ecology and Environment, Environmental Chemistry, Production (economics), Scenario analysis, Population Growth, education, Baseline (configuration management), Waste Management and Disposal, 0105 earth and related environmental sciences, Driving factors, Consumption (economics), education.field_of_study, Phosphorus, Urbanization, History, 20th Century, Models, Theoretical, Pollution, chemistry, Environmental science, Forecasting
Abstract

The phosphorus footprint (PF) is a novel concept to analyze human burdens on phosphorus resources. However, research on PF approach is still limited, and current several PF studies include incomplete phosphorus sources and have limited quantitative interpretation about the drivers of PF changes, which can help understand future trends of PF. This study develops a more comprehensive PF model by considering crop, livestock and aquatic food, and non-food goods, which covers the mainly phosphorus containing products consumed by human. The model is applied to quantify China's PF from 1961 to 2014, and the results of the model are also used to analyze the factors driving the PF changes and explored China's PF scenarios for 2050 using an econometric analysis model (STIRPAT). The result shows that China's PF increased over 11-fold, from 0.9 to 10.6 Tg between 1961 and 2014. The PF of livestock food dominated China's PF, accounting for 57% of the total in 1961 and 45% in 2014. The key factors driving the increase in China's PF are the increase in population and urbanization rate, with contributions of 38% and 33%, respectively. We showed that in the baseline scenario, China's PF would increase by 70% during 2014–2050 and cause the depletion of China's phosphate reserves in 2045. However, in the best case scenario, China's PF would decrease by 15% in 2050 compared with that in 2014, and it would have 50% of current phosphate reserve remaining by 2050. Several mitigation measures are then proposed by considering China's realities from both production and consumption perspective, which can provide valuable policy insights to other rapid developing countries to mitigate the P footprint.

Published
2019

44. Securing content-centric networks with content-based encryption

Author

You Zhang, Chun Yang, Haomiao Yang, Xin Cong, and Xiaofen Wang

Subjects
Computer Networks and Communications, Computer science, business.industry, Strong key, 020206 networking & telecommunications, 02 engineering and technology, Computer security model, Encryption, Computer security, computer.software_genre, Computer Science Applications, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), 020201 artificial intelligence & image processing, Semantic security, business, Replay attack, computer, Secure channel, Standard model (cryptography)
Abstract

As content is transmitted in content-driven manner in the content-centric network (CCN), it does not require any host address; therefore, it is infeasible to establish a traditional secure channel between hosts. Securing the content transmission in the CCN is a challenging problem. We solve this problem with the content-based encryption, where the encryption key is associated with the content itself, and the private decryption keys are distributed to the authorized consumers. To deal with the security requirements for content-based encryption, we define a security model that captures the key existential unforgeability and semantic security. We then propose a content-based encryption scheme with short ciphertexts, which is proven to be strong key existentially unforgeable and semantically secure in the standard model. We apply the scheme to construct a secure content transmission protocol in the CCN, which captures the security properties of content confidentiality, integrity, resistance to replay attacks and resistance to key forgery attacks. The performance analysis shows that our protocol is efficient for large content transmission.

Published
2019

45. Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton–proton collisions at s=13TeV with the ATLAS detector

Author

Kerstin Jon-And, Yanjun Tu, J. Silva, Kiyotomo Kawagoe, Daniel Guest, Yulia Rodina, Martin Hoeferkamp, Tairan Xu, Giuseppe Lerner, Carl Jeske, Michael Ughetto, Milos Lokajicek, F. Marroquim, Chris Sander, Anna Shcherbakova, Grigori Rybkin, Miroslav Myska, Michael Andrew Parker, Tyler James Burch, Edmund Dawe, Marco Sessa, Bellisario Esposito, Frank Merritt, Liang Guan, Tobias Bisanz, Remco Castelijn, Bojan Hiti, Wendy Taylor, Ralf Hertenberger, Peter Onyisi, Nimrod Taiblum, Till Eifert, K. K. Gan, R. Nagai, Blake Burghgrave, Evelin Meoni, Ivan Angelozzi, Stefan Stonjek, K. H. Yau Wong, A. Di Girolamo, Calin Alexa, P. Dita, Pedro Urrejola, Vakhtang Tsulaia, Michal Suk, Alexander Zaitsev, Petr Andreevich Gorbounov, Irakli Minashvili, Danuta Kisielewska, T. Vazquez Schroeder, James Boyd, Didier Ferrere, Takahiro Fusayasu, Odette Benary, Marcus Morgenstern, S. Donati, Abner Soffer, Marco Costa, Trisha Farooque, Steffen Stärz, Marco Bruschi, Lily Asquith, Mikhail Levchenko, M. Franklin, Paul Newman, T. Ženiš, Manuel Neumann, Philippe Calfayan, P. van Gemmeren, Henrik Oppen, Jing Wang, Juraj Bracinik, Jochen Meyer, I. Santoyo Castillo, Gabriel John Facini, Michel Janus, Valerio Bortolotto, Emily Nurse, Elizabeth Gallas, G. Costa, Yury Ryabov, Ioannis Gkialas, Dirk Duschinger, Nico Giangiacomi, Laurent Serin, Sau Lan Wu, Edgar Kellermann, Andrew Chisholm, Kaushik De, Damir Duvnjak, Dmitri Dedovich, Peter Davison, Georgios Iakovidis, Daniela Boerner, Mariya Shiyakova, Mikhail Demichev, Oleg Kuprash, Marcelo Vogel, A. Ezhilov, Rodger Mantifel, Ping-Kun Teng, Jan Stark, D. A. Soh, L. A. M. Wiik-Fuchs, Henry Lubatti, Marta Maria Perego, E. Yu. Soldatov, Raquel Pezoa, Louis D'eramo, K. O. H. Vadla, Carlo Schiavi, Allan G Clark, Roger Moore, Anna Macchiolo, Pavel Podberezko, Siegfried Bethke, Caterina Doglioni, Stanislav Tokár, Jean-Francois Grivaz, J. Ocariz, Philipp Mogg, Julia Isabell Djuvsland, Matteo Bedognetti, Kotaro Wakamiya, Simone Michele Mazza, Trine Poulsen, P. de Jong, Christopher Samuel Pollard, C. Malone, Mark Hodgkinson, Alena Loesle, Jiri Chudoba, D. M. Portillo Quintero, Tomoya Iizawa, Alan Peyaud, Magda Anna Chelstowska, Jorg Wotschack, Simone Sottocornola, Henso Abreu, Henrik Ohman, Y. Zhang, V. Boisvert, Stephanie Majewski, Tingting Wang, Matteo Mantoani, Stefania Spagnolo, Daniel Muenstermann, Marco Milesi, C. J. S. Young, G. Lehmann Miotto, Pietro Albicocco, Robert J. Bielski, Jun Su, Ren-You Zhang, Jelena Krstic, Joseph Haley, Nikola Lazar Whallon, Ruggero Turra, Mohamed Ouchrif, Cristiano Alpigiani, Hans Krüger, K. P. Mistry, Nicolas Maximilian Köhler, Alessandro Polini, Michael Ziolkowski, Alfred Goshaw, Roman Lysak, Michaela Mlynarikova, Christoph Eckardt, D. Amidei, Kun Liu, Kunihiro Nagano, Speranza Falciano, Olivier Arnaez, Carolyn Bertsche, A. Rodriguez Perez, Elliot Lipeles, Saleh Sultansoy, F. Wang, Y. Amaral Coutinho, Adrian Buzatu, Otmar Biebel, Amelia Jean Brennan, Ulrich Parzefall, Scott Snyder, Michael Homann, Carlos Lacasta, Masaya Ishino, R. Goncalves Gama, Michael Adersberger, Orjan Dale, Antonio Salvucci, Luiz Caloba, Laurent Schoeffel, Y. W. Liu, Alessandra Betti, Mateusz Dyndal, Ewelina Lobodzinska, H. B. Liu, William Brooks, Kerim Suruliz, Nikolaos Karastathis, Monika Wielers, Li Zhou, Eric Lancon, Wing Sheung Chan, Gerjan Bobbink, Alexey Boldyrev, Yuji Minegishi, J. W. Smith, Wladyslaw Dabrowski, Ehud Duchovni, Mario Antonelli, Kyungeon Choi, Elisa Guido, P. Teixeira-Dias, Antonia Strubig, Gian Luigi Alberghi, Jonathan Bortfeldt, David Berge, Evangelos Leonidas Gkougkousis, Pierre Marc Luzi, Sascha Mehlhase, Sergii Raspopov, Cyril Becot, Aviv Ruben Cukierman, Cunfeng Feng, Yasushi Nagasaka, R. J. Hawkings, Dave Lewis, Abbas Kenan Ciftci, Dave Robinson, Remi Lafaye, Radim Slovak, B. R. Mellado Garcia, Johannes Erdmann, Michael Ryan Clark, Christopher Willis, Manuela Campanelli, Matthew Edgar Leblanc, Elizabeth Brost, Alexandre Glazov, Tobias Flick, Gabriele D'amen, Laurent Vacavant, A. C. Taylor, Fumiaki Ito, Philip Sommer, Joe Taenzer, Andrea Catinaccio, Jens Weingarten, Robert Wolff, Andrej Filipcic, Marisilvia Donadelli, Yingchun Zhu, John Rutherfoord, F. Meloni, Val O'Shea, Darren Temple, Carl Gwilliam, Huacheng Cai, Reiner Hauser, Bruce Gallop, Xiaohu Sun, Sara Ghasemi, Alessandro Mirto, Philippe Mermod, Michael Stoebe, Michel Vetterli, Heidi Sandaker, Steve Muanza, R. Cherkaoui El Moursli, Dimitrios Sampsonidis, Imma Riu, Rebecca Anne Linck, J. Manjarres Ramos, Denys Denysiuk, F. De Lorenzi, Margherita Primavera, Manfredi Ronzani, Nicola Abraham, J. Machado Miguens, Hideyuki Oide, Dugan O'Neil, Enrico Pasqualucci, J. S. Webster, Daniel M. Levin, Stefan Simion, Andrea Sansoni, B. Alvarez Gonzalez, Fabian Wilk, Martine Bosman, Kevin Hildebrand, Diane Cinca, Bostjan Macek, Suyog Shrestha, Kalliopi Iordanidou, R. St. Denis, D. Vazquez Furelos, Sally Seidel, Irinel Caprini, Bing Zhou, Martin Kocian, R. Caminal Armadans, Renat Sadykov, Rudiger Voss, Silvia Franchino, A. De Santo, J. Van Nieuwkoop, Ph Gris, Jing-Dong Guo, Matt Zhang, Shaun Roe, Olga Rezanova, Samuel Calvet, David Martin Bjergaard, Mutsuto Hagihara, Marco Vanadia, Ingo Bloch, Reiner Klingenberg, Helmut Wolters, Carmen Maidantchik, Francesco Rubbo, Jacob Martin Pasner, Robert Astalos, Emmanuel Monnier, David Freeborn, Tigran Mkrtchyan, Evelyn Thomson, Johannes Elmsheuser, Klaudia Burka, J. Goncalves Pinto Firmino Da Costa, Lydia Roos, Isabelle Wingerter-Seez, Alessandro Tricoli, J. A. Dassoulas, Stefan Guindon, Fabian Spettel, Koichi Nagai, Sandra Kortner, Kazuya Mochizuki, Angela Maria Burger, Giancarlo Spigo, Kathleen Whalen, Peter Love, M. J. Da Cunha Sargedas De Sousa, Alexander Melzer, David Dodsworth, Helge Christoph Beck, Sergio Gonzalez-Sevilla, Oxana Smirnova, Athina Kourkoumeli-Charalampidi, Fabrizio Scuri, Sergey Peleganchuk, Ikuo Ueda, Christopher Lester, Remi Zaidan, Djamel Eddine Boumediene, Matthias Saimpert, James Catmore, Artur Trofymov, Ina Carli, Felix Rauscher, Peter Schacht, Richard Mount, Paolo Morettini, Marcin Pawel Guzik, Peter Loch, Fabienne Ledroit-Guillon, Konstantinos Bachas, Vadim Kantserov, Alan James Taylor, Philip Phillips, Samuel Silverstein, Chav Chhiv Chau, Teng Jian Khoo, Rostislav Konoplich, Alexandru Hostiuc, Kathy Pommès, Vojtech Pleskot, Mark Owen, Nikola Makovec, Joe Kroll, S. Argyropoulos, Andreas Redelbach, Alexander Paramonov, Priscilla Pani, Stephen Maxfield, Stephen Hillier, J. B. De Vivie De Regie, Nuno Filipe Castro, Andrew White, Haifeng Li, Luigi Marchese, Jan Kretzschmar, Farid Ould-Saada, Davide Caforio, Aaron Angerami, Miriam Diamond, Luis Hervas, Bjarne Stugu, Mykhailo Lisovyi, J. E. García Navarro, Giulio Cornelio Grossi, Matthew John Baca, Paul Laycock, John Penwell, Yu. A. Tikhonov, Jacob Searcy, Soren Stamm, Santiago Juan Batista, Giada Mancini, Robin Middleton, Abigail Alexandra O'Rourke, I. Ravinovich, Tatsumi Nitta, R. R. M. Fletcher, Tord Ekelof, Monica Verducci, Arnaud Ferrari, Marcia Begalli, Michal Simon, Steinar Stapnes, Ludovico Pontecorvo, Laura Rehnisch, Michael Pitt, Igor Aleksandrov, J. B. Liu, Ruo-yu Shang, Shunichi Akatsuka, Attila Krasznahorkay, R. Iuppa, Elodie Deborah Resseguie, Peter Berta, Masaki Ishitsuka, Miguel Arratia, Fengwangdong Zhang, Eleni Mountricha, Didier Lacour, Ondrej Penc, Silvia Resconi, Ka Wa Tsang, Stefan Wolf Weber, R. D. Schamberger, Pascal Pralavorio, Masato Aoki, Nicola Venturi, Stefan Schlenker, Jovan Mitrevski, Murrough Landon, Alexander Khodinov, Dominik Duda, Chiara Roda, O. Le Dortz, Liang Li, Francois Vazeille, Tina Sfiligoj, Bhaskar Dutta, Laurent Duflot, Dimitris Varouchas, Tobias Kupfer, Georgios Zacharis, Manolis Dris, Antonio Boveia, Gabriel Palacino, Juergen Kroseberg, A. T. Doyle, Douglas Benjamin, Nataliia Zakharchuk, Wouter Verkerke, Rhys Edward Owen, Alberto Cervelli, Torre Wenaus, Andrew Straiton Hard, Ljiljana Morvaj, Marjorie Shapiro, Soshi Tsuno, Tony Kwan, Louis Fayard, Benoit Lefebvre, Thorwald Klapdor-kleingrothaus, Savanna Marie Shaw, Carlo Varni, Margret Fincke-Keeler, Susan Cheatham, Sten Hellman, F. Salvatore, Torsten Harenberg, C. A. Solans Sanchez, S. 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Subjects
Quark, Physics, Nuclear and High Energy Physics, Large Hadron Collider, Photon, 010308 nuclear & particles physics, Astrophysics::High Energy Astrophysical Phenomena, Dark matter, Resonance, 01 natural sciences, Nuclear physics, 0103 physical sciences, Mass spectrum, Substructure, High Energy Physics::Experiment, 010306 general physics, Boson
Abstract

This Letter presents a search for new light resonances decaying to pairs of quarks and produced in association with a high- pT photon or jet. The dataset consists of proton–proton collisions with an integrated luminosity of 36.1 fb −1 at a centre-of-mass energy of s=13 TeV recorded by the ATLAS detector at the Large Hadron Collider. Resonance candidates are identified as massive large-radius jets with substructure consistent with a particle decaying into a quark pair. The mass spectrum of the candidates is examined for local excesses above background. No evidence of a new resonance is observed in the data, which are used to exclude the production of a lepto-phobic axial-vector Z′ boson.

Published
2019

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