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IL12A signal enhances efficacy of sorafenib plus NK cells immunotherapy to better suppress HCC progression (2016) Mol Cancer Ther, 15, pp. 731-742; Miura, K., Ishioka, M., Minami, S., Horie, Y., Ohshima, S., Goto, T., Toll-like receptor 4 on macrophage promotes the development of steatohepatitis-related hepatocellular carcinoma in mice (2016) J Biol Chem, 291, pp. 11504-11517; Miura, K., Ohnishi, H., Role of gut microbiota and toll-like receptors in nonalcoholic fatty liver disease (2014) World J Gastroenterol, 20, pp. 7381-7391; Dapito, D.H., Mencin, A., Gwak, G.Y., Pradere, J.P., Jang, M.K., Mederacke, I., Promotion of hepatocellular carcinoma by the intestinal microbiota and TLR4 (2012) Cancer Cell, 21, pp. 504-516; Li, W., Xiao, J., Zhou, X., Xu, M., Hu, C., Xu, X., STK4 regulates TLR pathways and protects against chronic inflammation-related hepatocellular carcinoma (2015) J Clin Invest, 125, pp. 4239-4254; Uthaya Kumar, D.B., Chen, C.L., Liu, J.C., Feldman, D.E., Sher, L.S., French, S., TLR4 signaling via NAnoG cooperates with STAT3 to activate twist1 and promote formation of tumor-initiating stem-like cells in livers of mice (2016) Gastroenterology, 150, pp. 707-719; Sepehri, Z., Kiani, Z., Kohan, F., Alavian, S.M., Ghavami, S., Toll like receptor 4 and hepatocellular carcinoma; a systematic review (2017) Life Sci, 179, pp. 80-87; Lin, A., Wang, G., Zhao, H., Zhang, Y., Han, Q., Zhang, C., TLR4 signaling promotes a COX-2/PGE2/STAT3 positive feedback loop in hepatocellular carcinoma (HCC) cells (2016) Oncoimmunology, 5, p. e1074376; Shi, G., Wang, C., Zhang, P., Ji, L., Xu, S., Tan, X., Donor polymorphisms of toll-like receptor 4 rs1927914 associated with the risk of hepatocellular carcinoma recurrence following liver transplantation (2017) Arch Med Res, 48, pp. 553-560; Xu, D., Han, Q., Hou, Z., Zhang, C., Zhang, J., MiR-146a negatively regulates NK cell functions via STAT1 signaling (2017) Cell Mol Immunol, 14, pp. 712-720; Lan, P., Zhang, C., Han, Q., Zhang, J., Tian, Z., Therapeutic recovery of hepatitis b virus (HBV)-induced hepatocyte-intrinsic immune defect reverses systemic adaptive immune tolerance (2013) Hepatology, 58, pp. 73-85; Shalini, S., Nikolic, A., Wilson, C.H., Puccini, J., Sladojevic, N., Finnie, J., Caspase-2 deficiency accelerates chemically induced liver cancer in mice (2016) Cell Death Differ, 23, pp. 1727-1736; Wang, Y., Cai, J., Zeng, X., Chen, Y., Yan, W., Ouyang, Y., Downregulation of toll-like receptor 4 induces suppressive effects on hepatitis b virus-related hepatocellular carcinoma via ERK1/2 signaling (2015) BMC Cancer, 15, p. 821; Hartwell, H.J., Petrosky, K.Y., Fox, J.G., Horseman, N.D., Rogers, A.B., Prolactin prevents hepatocellular carcinoma by restricting innate immune activation of c-myc in mice (2014) Proc Natl Acad Sci U S A, 111, pp. 11455-11460; van de Wijngaart, D.J., Dubbink, H.J., van Royen, M.E., Trapman, J., Jenster, G., Androgen receptor coregulators: Recruitment via the coactivator binding groove (2012) Mol Cell Endocrinol, 352, pp. 57-69; Heemers, H.V., Tindall, D.J., Androgen receptor (AR) coregulators: A diversity of functions converging on and regulating the AR transcriptional complex (2007) Endocr Rev, 28, pp. 778-808; Shi, L., Yan, P., Liang, Y., Sun, Y., Shen, J., Zhou, S., Circular RNA expression is suppressed by androgen receptor (AR)-regulated adenosine deaminase that acts on RNA (ADAR1) in human hepatocellular carcinoma (2017) Cell Death Dis, 8, p. e3171; Shi, L., Lin, H., Li, G., Sun, Y., Shen, J., Xu, J., Cisplatin enhances NK cells immunotherapy efficacy to suppress HCC progression via altering the androgen receptor (AR)-ULBP2 signals (2016) Cancer Lett, 373, pp. 45-56; Jiang, X., Kanda, T., Nakamoto, S., Miyamura, T., Wu, S., Yokosuka, O., Involvement of androgen receptor and glucose-regulated protein 78 kda in human hepatocarcinogenesis (2014) Exp Cell Res, 323, pp. 326-336; Ngo, H.K.C., Kim, D.H., Cha, Y.N., Na, H.K., Surh, Y.J., Nrf2 mutagenic activation drives hepatocarcinogenesis (2017) Cancer Res, 77, pp. 4797-4808; Lin, S.J., Chou, F.J., Li, L., Lin, C.Y., Yeh, S., Chang, C., Natural killer cells suppress enzalutamide resistance and cell invasion in the castration resistant prostate cancer via targeting the androgen receptor splicing variant 7 (ARv7) (2017) Cancer Lett, 398, pp. 62-69; Cheng, M.A., Chou, F.J., Wang, K., Yang, R., Ding, J., Zhang, Q., Androgen receptor (AR) degradation enhancer ASC-J9((R)) in an FDA-approved formulated solution suppresses castration resistant prostate cancer cell growth (2018) Cancer Lett, 417, pp. 182-191 [format_title_en_publication_en_pub_year] => 6f56bdad5a2e3529eb4394694f4b5f1e456480166 [abstract_en] => Background: Androgen receptor (AR) has a role in regulating malignancies and gender disparities in hepatocellular carcinoma (HCC). Recently, TLR4 activation is demonstrated to be required for HCC progression; however, whether and how TLR4 interacts with AR is largely unknown.; Methods: The tumorigenesis was detected in female and male mice induced by DEN/CCL4, then TLR4 and AR signals were detected in liver tissues by qPCR and FACS. The proliferation, colony formation and migration of HCC cell treated with TLR4 agonist LPS, or/and androgen DHT were evaluated in vitro. Furthermore, the expression of TLR4 and AR was detected by IHC in tissue microarray of HCC, and correlation of AR and TLR4 was defined.; Results: Male mice are more susceptible to develop HCC than female mice. Meanwhile, we found baseline TLR4 levels were higher in male mice than in female mice. AR expression in male mice was increased by treatment with DEN/CCL4. And, AR was constitutively expressed in human HCC cell lines. Dihydrotestosterone (DHT) stimulated TLR4 expression in both HepG2 and HepG2 2.15 cells, which could be blocked by silencing AR. On the other hand, treatment with LPS stimulated AR expression, but it was blocked by treatment with TLR4 antagonist and in cells deficient for TLR4. DHT treatment exacerbated TLR4-induced cellular proliferation, colony formation, migration, and invasion of HepG2 cells. The positive relationship between AR and TLR4 was confirmed in human HCC samples.; Conclusions: DHT-AR-TLR4 signaling enhances the development of HCC cells and facilitates their migration and invasion, demonstrating a mechanism underlying gender disparity in HCC. [scopus_id] => 55416640400;57213836522;57204707893;36018041700; [from_id] => 76,73 [cauthor_ad] => [Zhang, J]Shandong Univ, Sch Pharmaceut Sci, Inst Immunopharmaceut Sci, Jinan 250012, Shandong, Peoples R China. [hx_id] => 2378,2371 [doi] => 10.7150/jca.30682 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [format_issn_issue_page_pub_year] => 1673065a8cc6ffc1db822699bcaaa4e0-1925113239 [title_en] => Androgen Receptor (AR)-TLR4 Crosstalk Mediates Gender Disparities in Hepatocellular Carcinoma Incidence and Progression [index_keyword] => androgen receptor; androstanolone; carbon tetrachloride; lipopolysaccharide; messenger RNA; toll like receptor 4; animal cell; animal experiment; animal model; animal tissue; Article; cancer growth; cancer incidence; carcinogenesis; cell invasion; cell migration; cell proliferation; colony formation; controlled study; female; fluorescence activated cell sorting; human; human tissue; immunofluorescence test; liver cell; liver cell carcinoma; liver tissue; male; mouse; nonhuman; protein expression; real time polymerase chain reaction; sex difference; tissue microarray; Western blotting [volume] => 11 [source_type] => 351 [pub_year] => 2020 [keyword_en] => AR; Gender bias; TLR4; Hepatocellular Carcinoma [article_id] => 819421,812476 [begin_page] => 1094 [hints] => 0 [publisher] => IVYSPRING INT PUBL [substance] => androstanolone, 521-18-6; carbon tetrachloride, 56-23-5; toll like receptor 4, 203811-83-0 [language] => English [issue] => 5 [issn] => 1837-9664 [batch] => 3422,3424 [publication_en] => JOURNAL OF CANCER [email] => zhangj65@sdu.edu.cn [sys_update_time] => 2020-03-13 09:56:09 [format_title_en_issn_pub_year] => d6e8382dbb08ac734048c1de2d619426150854751 [publication_iso] => J. Cancer [SYS_TAG] => 3 [end_page] => 1103 [page] => 1094-1103 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [cite_wos] => 0 [check_3Y] => 0 [delivery_No] => JW1OU [format_title] => [author_fn] => Han, Qiuju; Yang, Dan; Yin, Chunlai; Zhang, Jian [pages] => 10 [publication_29] => J CANCER [open_type] => DOAJ Gold, Green Published [pubmedID] => 31956356 [publication_type] => J [get_data] => 2020-03-06 [format_publication_cn] => [keyword_plu] => RESISTANT PROSTATE-CANCER; NK CELLS IMMUNOTHERAPY; HEPATITIS-B-VIRUS; ACTIVATION; HCC; COREGULATORS; MICROBIOTA; PROMOTES; EFFICACY [fund_ab] => This work was supported Shandong Provincial Key Research and Development; Program [grant number 2017GSF18159] and Shandong Provincial Natural; Science Foundation, China [grant number ZR2017BH029] and the Fundamental; Research Fund of Shandong University (2017JC004). [format_title_en] => 81ad23dd8726890242e9d6dabf51d327231092549 [publisher_city] => LAKE HAVEN [cauthor_order] => 4 [reference_No] => 34 [cite_awos] => 0 [wos_No] => WOS:000502829400011 [sys_priority_field] => 73 [format_wos_No] => 7a35e2d1c7d068cb8a18bd99f6a169d0-1232514087 [wos_sub] => Oncology [research_area] => Oncology [cauthor_back] => Zhang, J [check_180] => 0 [publisher_ad] => PO BOX 4546, LAKE HAVEN, NSW 2263, AUSTRALIA [format_publication_en] => dad2f4de504f5b4b9d946e945cae5e5c1034412683 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => journalofcancer [jl_country] => 中国 [jl_keyword_en] => ar,tlr4,genderbias,hepatocellularcarcinoma [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => ivyspringintpubl [company_id] => 0,174 [author_id] => 21049,25453,25452,25457,25456,25455,25445,25446,25447 [author_test] => Array ( [0] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 1 [name] => 韩秋菊 [irtag] => 7 [t_index] => 0 [person_id] => 21049 ) [1] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张健 [irtag] => 7 [t_index] => 4 [person_id] => 25457 ) [2] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张剑 [irtag] => 7 [t_index] => 4 [person_id] => 25453 ) [3] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张健 [irtag] => 7 [t_index] => 4 [person_id] => 25455 ) [4] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张健 [irtag] => 7 [t_index] => 4 [person_id] => 25456 ) [5] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张剑 [irtag] => 7 [t_index] => 4 [person_id] => 25452 ) [6] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张嘉宁 [irtag] => 7 [t_index] => 4 [person_id] => 25445 ) [7] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张建 [irtag] => 7 [t_index] => 4 [person_id] => 25446 ) [8] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 4 [name] => 张建 [irtag] => 7 [t_index] => 4 [person_id] => 25447 ) ) [sys_subject_sort] => 0 [college_parent_id] => 174 [company_test] => Array [id] => RAA003ABe-eYmRww6g3I [tags] => 0 ) [4] => Array ( [standard_in] => School of Software, Shandong University, Jinan, Shandong, 250101, China; State Grid Anhui Electric Power Company, Hefei, Anhui, 230061, China; First Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250014, China [cauthor] => Huang, Y(huang_yan74@163.com) [school_id] => 117 [scopus_No] => 2-s2.0-85079398959 [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079398959&doi=10.1155%2f2020%2f1782531&partnerID=40&md5=1a78a4b5021899c45d01c40374e5b15b [tag] => 0 [author_en] => Sun, LM; Kong, Q; Huang, Y; Yang, JS; Wang, SS; Zou, RQ; Yin, YL; Peng, JL [format_scopus_No] => 36dbaad2f23b5de8e73be1358cb892a5462395728 [format_doi] => d1c5b901831682136764b3ea5270d911-1794586774 [sys_update_time] => 2020-03-13 09:56:09 [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [61872398] [reference] => Subburaj, K., Ravi, B., Agarwal, M., Automated identification of anatomical landmarks on 3d bone models reconstructed from CT scan images (2009) Computerized Medical Imaging and Graphics, 33 (5), pp. 359-368. , 2-s2.0-67349129682; 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Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. The proposed quantities are proved valid and the proposed algorithms are proved effective by experiments. [scopus_id] => 57214882697;56103969300;57214880764;57212326631;56104118800;56104051700;8981026100;7401958611; [from_id] => 76,73 [cauthor_ad] => [Huang, Y]Shandong Univ, Sch Software, Jinan 250101, Shandong, Peoples R China@@@[Yang, JS]Shandong Univ Tradit Chinese Med, Affiliated Hosp 1, Jinan 250014, Shandong, Peoples R China. [hx_id] => 2378,2371 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [title_en] => Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis [author_in] => [Sun, Limin; Huang, Yan; Yin, Yilong; Peng, Jingliang] Shandong Univ, Sch Software, Jinan 250101, Shandong, Peoples R China.@@@ [Kong, Qi] State Grid Anhui Elect Power Co, Hefei 230061, Anhui, Peoples R China.@@@ [Yang, Jiushan; Wang, Shaoshan; Zou, Ruiqi] Shandong Univ Tradit Chinese Med, Affiliated Hosp 1, Jinan 250014, Shandong, Peoples R China. [volume] => 2020 [source_type] => 351 [pub_year] => 2020 [article_id] => 819359,813444 [hints] => 1 [publisher] => HINDAWI LTD [doi] => 10.1155/2020/1782531 [language] => English [issn] => 1748-670X [batch] => 3422,3424 [publication_en] => COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE [email] => sunliminsdu@163.com; lindaxian@yeah.net; huang_yan74@163.com;; yangjiushan@163.com; shaoshan278@sohu.com; zrq197935@163.com;; ylyin@sdu.edu.cn; jingliap@163.com [document_No] => 1782531 [format_title_en_issn_pub_year] => 2ecc7093c334a1fd2bd45bdae955e8c4687151721 [publication_iso] => Comput. Math. Method Med. [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [cite_wos] => 0 [check_3Y] => 0 [delivery_No] => KN1LE [format_title] => [author_fn] => Sun, Limin; Kong, Qi; Huang, Yan; Yang, Jiushan; Wang, Shaoshan; Zou, Ruiqi; Yin, Yilong; Peng, Jingliang [pages] => 13 [publication_29] => COMPUT MATH METHOD M [open_type] => DOAJ Gold [eissn] => 1748-6718 [publication_type] => J [get_data] => 2020-03-06 [format_publication_cn] => [keyword_plu] => ACTIVE CONTOURS DRIVEN; BIAS FIELD ESTIMATION [fund_ab] => The authors thank Xian Wu for his help in rendering the images for; Figure 2. This work was supported by the National Natural Science; Foundation of China (grant no. 61872398). [format_title_en] => 163c222f083bcd3d358cc7a37e6e282f1416914484 [publisher_city] => LONDON [pub_date] => JAN 28 [cauthor_order] => 3,4 [reference_No] => 36 [cite_awos] => 0 [wos_No] => WOS:000514600200001 [sys_priority_field] => 73 [format_wos_No] => 662c98a398061ebfd89c80238ef424aa-1440434370 [wos_sub] => Mathematical & Computational Biology [research_area] => Mathematical & Computational Biology [cauthor_back] => Huang, Y@@@Yang, JS [check_180] => 0 [publisher_ad] => ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND [format_publication_en] => 10d802888c1b0289d64f731bb0ed1c561299717500 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => computationalandmathematicalmethodsinmedicine [jl_country] => 中国 [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => hindawiltd [company_id] => 0 [author_id] => 23020,25114,21296 [author_test] => Array ( [0] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 3 [name] => 黄燕 [irtag] => 7 [t_index] => 3 [person_id] => 21296 ) [1] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 8 [name] => 彭京亮 [irtag] => 7 [t_index] => 0 [person_id] => 23020 ) [2] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 7 [name] => 尹义龙 [irtag] => 7 [t_index] => 0 [person_id] => 25114 ) ) [id] => BgA003ABe-eYmRww6A0C [tags] => 0 ) [5] => Array ( [cauthor] => Cui, Chaoran(crcui@sdufe.edu.cn) [issn] => 0020-0255 [school_id] => 117 [controlled_terms] => Collaborative filtering - Deep neural networks - Network architecture [batch2] => 15 [hb_batch] => 3418 [ei_No] => 20194307587525 [tag] => 0 [author_en] => Cui, CR; Yang, WY; Shi, C; Wang, M; Nie, XS; Yin, YL [abstract_en] => Personalization is emerging as a key research objective for image aesthetics assessment, and how to incorporate personal preferences into aesthetics models is a crucial issue to be solved. Prior studies usually require users to explicitly express their aesthetic preferences in certain ways, which are time-consuming and labor-intensive. In this paper, inspired by the observation that human cognition and behavior influence each other, we propose to sense user aesthetic preferences from their favoring behavior on social media platforms. In this manner, personalized image aesthetics assessment can be realized without adding any extra burden to users. Towards this goal, we gather user favoring behavior over professional social photos and consider both user personal preference and common aesthetic standard to deal with the unreliability of user favoring behavior. Besides, we follow the idea of collaborative filtering and optimize the pairwise ranking between images to alleviate the data sparsity problem. Finally, a deep neural network architecture is developed for personalized aesthetics modeling. A simulated evaluation is carried out on two benchmark aesthetics datasets, even though users\' true preferences cannot be directly observed. The results demonstrate the potential of our approach for personalized image aesthetics assessment. (C) 2019 Elsevier Inc. All rights reserved. [format_doi] => 2e057d2042117c41c871550ca534d886-877719172 [author_in] => [Cui, Chaoran; Yang, Wenya; Nie, Xiushan] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Shandong, Peoples R China.@@@ [Shi, Cheng] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China.@@@ [Wang, Meng] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230601, Anhui, Peoples R China.@@@ [Yin, Yilong] Shandong Univ, Sch Software, Jinan 250101, Shandong, Peoples R China. [cauthor_back] => Cui, Chaoran@@@Cui, CR [format_title_en_publication_en_pub_year] => 129e38e20865d40cdcbf8f894a3db7761500262005 [classification_No] => 903.1 Information Sources and Analysis - 971 Social Sciences [main_eword] => Behavioral research [format_ei_No] => ea9a87ffea187e2b830a954365eb0e4e-284876897 [from_id] => 74,73 [email] => crcui@sdufe.edu.cn; niexsh@sdufe.edu.cn; ylyin@sdu.edu.cn [classification_pub] => ISIJBC [datebase] => Compendex;Compilation and indexing terms, Copyright 2020 Elsevier Inc. [sys_level_num] => 15_8 [sys_jg_type] => 11,9 [title_en] => Personalized image quality assessment with Social-Sensed aesthetic preference [volume] => 512 [author_fn] => Cui, Chaoran; Yang, Wenya; Shi, Cheng; Wang, Meng; Nie, Xiushan; Yin, Yilong [pub_year] => 2020 [pub_date] => FEB [begin_page] => 780 [hints] => 1 [publisher] => ELSEVIER SCIENCE INC [doi] => 10.1016/j.ins.2019.10.011 [language] => English [source_type] => 351 [reference_No] => 50 [batch] => 3422,3418 [publication_en] => INFORMATION SCIENCES [hx_id] => 2376,2371 [sys_update_time] => 2020-03-13 09:40:15 [format_title_en_issn_pub_year] => 32c145a375baf5326cbc4648c9c3f963-1078416611 [article_id] => 813703,809727 [cauthor_order] => 1,1 [uncontrolled_terms] => Aesthetic preference - Data sparsity problems - Image Aesthetics - Image quality assessment - Personal preferences - Personalizations - Social media platforms - Social sense [SYS_TAG] => 3 [end_page] => 794 [page] => 780-794 [hb_type] => 2 [article_dt] => Article [cite_wos] => 0 [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [61701281, 61876098, 61671274, 61573219]; Shandong; Provincial Natural Science FoundationNatural Science Foundation of; Shandong Province [ZR2017QF009]; Fostering Project of Dominant; Discipline and Talent Team of Shandong Province Higher Education; Institutions [check_3Y] => 4 [delivery_No] => JZ0ET [format_title] => [cauthor_ad] => [Cui, CR]Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Shandong, Peoples R China. [pages] => 15 [publication_29] => INFORM SCIENCES [eissn] => 1872-6291 [publication_type] => J [get_data] => 2020-03-06 [keyword_en] => Image aesthetics assessment; Personalization; Aesthetic preference; modeling; Social sense [format_publication_cn] => [publication_iso] => Inf. Sci. [fund_ab] => This work was supported by the National Natural Science Foundation of; China under Grant 61701281, Grant 61876098, Grant 61671274, and Grant; 61573219, by Shandong Provincial Natural Science Foundation under Grant; ZR2017QF009, and by the Fostering Project of Dominant Discipline and; Talent Team of Shandong Province Higher Education Institutions. [format_title_en] => f126ff6a3b4de0b5cee6aae520b0d5ef-839600605 [publisher_city] => NEW YORK [cite_awos] => 0 [wos_No] => WOS:000504778300050 [sys_priority_field] => 73 [format_wos_No] => 4b93e1d5360d72ec7844c781ea38cc1f-2066273099 [wos_sub] => Computer Science, Information Systems [research_area] => Computer Science [check_180] => 4 [publisher_ad] => STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA [format_publication_en] => b7c57e46a19d28856337bb8f79a7b12c-1440894452 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => informationsciences [jl_country] => 中国 [jl_keyword_en] => imageaestheticsassessment,aestheticpreference,modeling,socialsense,personalization [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elsevierscienceinc [company_id] => 0,163 [author_id] => 25114 [author_test] => Array ( [0] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 6 [name] => 尹义龙 [irtag] => 7 [t_index] => 0 [person_id] => 25114 ) ) [sys_subject_sort] => 0 [college_parent_id] => 163 [company_test] => Array [id] => dwA303ABe-eYmRww8iK5 [tags] => 0 ) [6] => Array ( [cite_scopus] => 1 [cauthor] => Zou, G(zouguizheng@sdu.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85075377230 [author_in] => [Gao, Xuwen; Fu, Kena; Fu, Li; Zhang, Bin; Zou, Guizheng] Shandong Univ, Sch Chem & Chem Engn, Jinan 250100, Peoples R China.@@@ [Wang, Huaisheng] Liaocheng Univ, Dept Chem, Liaocheng 252059, Shandong, Peoples R China. [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075377230&doi=10.1016%2fj.bios.2019.111880&partnerID=40&md5=412a7be2afd20d07b8a2a899d940c4c3 [tag] => 0 [author_en] => Gao, XW; Fu, KN; Fu, L; Wang, HS; Zhang, B; Zou, GZ [publication_en] => BIOSENSORS & BIOELECTRONICS [format_scopus_No] => 677de94982cd80c0a617b8e82e5e991c414166480 [format_doi] => 0e5018bed3cb929a8e859e4d53deb40e-1085060898 [sys_update_time] => 2020-03-13 09:56:31 [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [21427808, 21375077]; Fundamental Research Funds of; Shandong University [2018JC017] [reference] => Cao, J.T., Wang, Y.L., Zhang, J.J., Zhou, Y.J., Ren, S.W., Liu, Y.M., (2016) RSC Adv., 6 (89), pp. 86682-86687; Chen, C., Zhang, P.F., Gao, G.H., Gao, D.Y., Yang, Y., Liu, H., Wang, Y.H., Cai, L.T., (2014) Adv. 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Chem., 89 (23), pp. 13024-13029 [format_title_en_publication_en_pub_year] => b5bf210b39fa6123cd87672f0befbf7b-1626319676 [abstract_en] => Novel optical labels for biosensing in near-infrared (NIR) region (especially between 800 and 900 nm) are arousing much attention for higher penetrating capability, less scattering and lowered autofluorescent background. Herein, a water-soluble electrochemiluminophore with effective electrochemiluminescence (ECL) around 815 nm is developed via doping dual-stabilizers-capped CdTe nanocrystals (NCs) with Co2+ species in a growth-doping way. The Co2+-doped CdTe NCs not only can preserve the highly-passivated surface states of dual-stabilizers-capped CdTe NCs, but also exhibit efficient red-shifted photoluminescence (PL) and ECL into the promising optical NIR window of 800-900 nm. A spectrum-based ultrasensitive NIR ECL immunosensor is consequently fabricated with the Co2+-doped CdTe NCs as tags for the first time, which can selectively and sensitively determine human carcinoembryonic antigen with a wide linearity range from 1 fg/mL to 10 pg/mL and a low limit of detection at 0.2 fg/mL (S/N = 3). This work opens a way to screen novel NIR electrochemiluminophore as well as to modulate the ECL performance of NCs via surface doping and engineering. [scopus_id] => 57207302028;57202136464;57202130521;56922351200;57202240396;57102652800; [from_id] => 76,73 [cauthor_ad] => [Zou, GZ]Shandong Univ, Sch Chem & Chem Engn, Jinan 250100, Peoples R China. [hx_id] => 2378,2371 [classification_pub] => BBIOE [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11,10 [title_en] => Red-shifted electrochemiluminescence of CdTe nanocrystals via Co2+-Doping and its spectral sensing application in near-infrared region [index_keyword] => Antigens; Cadmium telluride; II-VI semiconductors; Immunosensors; Infrared devices; Nanocrystals; Carcinoembryonic antigen; CdTe nanocrystals; CdTe NCs; Electrochemiluminescence; Limit of detection; Near Infrared; Near infrared region; Passivated surface; Red Shift [standard_in] => School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China; Department of Chemistry, Liaocheng University, Liaocheng, 252059, China [volume] => 150 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Electrochemiluminescence; Near-infrared; Co2+-doped CdTe NCs;; Immunosensor; Human carcinoembryonic antigen [article_id] => 810408,814834 [hints] => 0 [publisher] => ELSEVIER ADVANCED TECHNOLOGY [doi] => 10.1016/j.bios.2019.111880 [language] => English [issn] => 0956-5663 [batch] => 3422,3424 [pubmedID] => 31748194 [email] => zouguizheng@sdu.edu.cn [document_No] => 111880 [format_title_en_issn_pub_year] => c7e99ef415b70697536af4d77b53dcef971132626 [publication_iso] => Biosens. Bioelectron. [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [cite_wos] => 0 [check_3Y] => 33 [delivery_No] => KG0MO [format_title] => [author_fn] => Gao, Xuwen; Fu, Kena; Fu, Li; Wang, Huaisheng; Zhang, Bin; Zou, Guizheng [pages] => 7 [publication_29] => BIOSENS BIOELECTRON [researcherID] => Zhang, Bin/H-4942-2014 [eissn] => 1873-4235 [orcID] => Zhang, Bin/0000-0002-1529-6356 [publication_type] => J [get_data] => 2020-03-06 [format_publication_cn] => [keyword_plu] => DUAL-COLOR ELECTROCHEMILUMINESCENCE; ELECTROGENERATED CHEMILUMINESCENCE; QUANTUM DOTS; EFFICIENT ELECTROCHEMILUMINESCENCE; FLUORESCENT-PROBES; IMMUNOASSAY; ELECTROCHEMISTRY; APTASENSOR; STRATEGY; ANTIGEN [fund_ab] => This project is supported by the National Natural Science Foundation of; China (Grant Nos. 21427808, 21375077), and the Fundamental Research; Funds of Shandong University (2018JC017). [format_title_en] => 8f5d8515c441d1e4b5e77867297a83f8-1226712364 [publisher_city] => OXFORD [pub_date] => FEB 15 [cauthor_order] => 6 [reference_No] => 57 [cite_awos] => 0 [wos_No] => WOS:000509635500049 [sys_priority_field] => 73 [format_wos_No] => 3ce361f7054c09b42669271358a12aef894547588 [wos_sub] => Biophysics; Biotechnology & Applied Microbiology; Chemistry, Analytical;; Electrochemistry; Nanoscience & Nanotechnology [research_area] => Biophysics; Biotechnology & Applied Microbiology; Chemistry;; Electrochemistry; Science & Technology - Other Topics [cauthor_back] => Zou, GZ [check_180] => 33 [publisher_ad] => OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON,; OXFORD OX5 1GB, OXON, ENGLAND [format_publication_en] => 1a07e3b34981827aeb11821a0da313f71797155525 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => biosensorsandbioelectronics [jl_country] => 中国 [jl_keyword_en] => ,co2dopedcdtencs,nearinfrared,humancarcinoembryonicantigen,electrochemiluminescence,immunosensor [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elsevieradvancedtechnology [author_test] => Array ( [0] => Array ( [sure] => 1 [irmagnum] => 0 [u_index] => 0 [name] => 邹桂征 [irtag] => 0 [t_index] => 0 [person_id] => 26251 ) [1] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 3 [name] => 傅利 [irtag] => 7 [t_index] => 0 [person_id] => 20761 ) [2] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 5 [name] => 张斌 [irtag] => 7 [t_index] => 0 [person_id] => 25314 ) [3] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 5 [name] => 张斌 [irtag] => 7 [t_index] => 0 [person_id] => 25315 ) [4] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 5 [name] => 张斌 [irtag] => 7 [t_index] => 0 [person_id] => 25312 ) [5] => Array ( [sure] => 0 [irmagnum] => 0 [u_index] => 5 [name] => 张斌 [sys_author_id] => Array ( [0] => 26251 ) [irtag] => 7 [t_index] => 0 [person_id] => 25313 ) ) [company_id] => 0,169 [author_id] => 25312,25313,25314,25315,26251,20761 [sys_subject_sort] => 0 [college_parent_id] => 169 [company_test] => Array [id] => IQA103ABe-eYmRwwJhsm [tags] => 0 ) [7] => Array ( [standard_in] => Institute for Advanced Manufacturing, Shandong University of Technology, Zibo, China [cauthor] => Zhao, Guoyong(zgy709@126.com) [school_id] => 117 [scopus_No] => 2-s2.0-85078464306 [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078464306&doi=10.1080%2f0951192X.2020.1718763&partnerID=40&md5=8c98db9a58151cf3f03c2e7459c91fd3 [tag] => 0 [author_en] => Zhao, GY; Li, CX; Lv, Z; Cheng, X; Zheng, GM [format_scopus_No] => fbedee7ab07888b263d89f08b6e5ce8f-2010847689 [format_doi] => 4ba0e8dbd0fe8877c60260c933b49b16-1296476246 [author_in] => [Zhao, Guoyong; Li, Chunxiao; Lv, Zhe; Cheng, Xiang; Zheng, Guangming] Shandong Univ Technol, Inst Adv Mfg, Zibo, Peoples R China. [fund_No] => Project of Shandong Province key research development of China; [2017GGX30114]; Project of Shandong Province Natural Science Foundation; of China [ZR2016EEM29] [reference] => Balogun, V.A., Mativenga, P.T., Modelling of Direct Energy Requirements in Mechanical Machining Processes (2013) Journal of Cleaner Production, 41, pp. 179-186; David, A.G.Z., Abdullah, A.M., Germanico, G.B., An Energy Consumption Approach in a Manufacturing Process Using Design of Experiments (2018) International Journal of Computer Integrated Manufacturing, 31 (11), pp. 1067-1077; Duflou, J.R., Sutherland, J.W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., Hauschild, M., Kellens, K., Towards Energy and Resource Efficient Manufacturing: Aprocesses and Systems Approach (2012) CIRP Annals - Manufacturing Technology, 61 (2), pp. 587-609; Garg, A., Lam, J.S.L., Gao, L., Power Consumption and Tool Life Models for the Production Process (2016) Journal of Cleaner Production, 131, pp. 754-764; He, Y., Liu, B., Zhang, X.D., Gao, H., Liu, X.H., A Modeling Method of Task-oriented Energy Consumption for Machining Manufacturing System (2012) Journal of Cleaner Production, 23 (1), pp. 167-174; Jia, S., Tang, R.Z., Lv, J.X., Machining Activity Extraction and Energy Attributes Inheritance Method to Support Intelligent Energy Estimation of Machining Process (2016) Journal of Intelligent Manufacturing, 27 (3), pp. 595-616; Jia, S., Tang, R.Z., Lv, J.X., Yuan, Q.H., Peng, T., Energy Consumption Modeling of Machining Transient States Based on Finite State Machine (2017) International Journal of Advanced Manufacturing Technology, 88 (5-8), pp. 2305-2320; Kara, S., Li, W., Unit Process Energy Consumption Models for Material Removal Processes (2011) CIRP Annals - Manufacturing Technology, 60 (1), pp. 37-40; Li, L., Yan, J.H., Xing, Z.W., Energy Requirements Evaluation of Milling Machines Based on Thermal Equilibrium and Empirical Modeling (2013) Journal of Cleaner Production, 52, pp. 113-121; Li, T., Kong, L.L., Zhang, H.C., Asif, I., Recent Research and Development of Typical Cutting Machine Tool’s Energy Consumption Model (2014) Journal of Mechanical Engineering, 50 (7), pp. 102-111. , in chinese, and; Liu, F., Wang, Q.L., Liu, G.J., Content Architecture and Future Trends of Energy Efficiency Research on Machining Systems (2013) Journal of Mechanical Engineering, 49 (19), pp. 87-94; Liu, F., Xie, J., Liu, S., A Method for Predicting the Energy Consumption of the Main Driving System of A Machine Tool in A Machining Process (2015) Journal of Cleaner Production, 105, pp. 171-177; Lv, J.X., Tang, R.Z., Tang, W.C.J., Liu, Y., Zhang, Y.F., Jia, S., An Investigation into Reducing the Spindle Acceleration Energy Consumption of Machine Tools (2017) Journal of Cleaner Production, 143, pp. 794-803; Ma, F., Zhang, H., Cao, H.J., Hon, K.K.B., An Energy Consumption Optimization Strategy for CNC Milling (2017) International Journal of Advanced Manufacturing Technology, 90 (5-8), pp. 1715-1726; Mori, M., Fujishima, M., Inamasu, Y., Oda, Y., A Study on Energy Efficiency Improvement for Machine Tools (2011) CIRP Annals - Manufacturing Technology, 60 (1), pp. 145-148; Newman, S.T., Nassehi, A., Imani-Asrai, R., Dhokia, V., Energy Efficient Process Planning for CNC Machining (2012) CIRP Journal of Manufacturing Science and Technology, 5 (2), pp. 127-136; Quintana, G., Ciurana, J., Ribatallada, J., Modelling Power Consumption in Ball-end Milling Operations (2011) Materials and Manufacturing Processes, 26 (5), pp. 746-756; Seow, Y., Rahimifard, S., Woolley, E., Simulation of Energy Consumption in the Manufacture of a Product (2013) International Journal of Computer Integrated Manufacturing, 26 (7), pp. 663-680; Vasco, D.G., José, A.O.L., João, F.M., Energy Consumption Awareness in Manufacturing and Production Systems (2017) International Journal of Computer Integrated Manufacturing, 30 (1), pp. 84-95; Wang, X.L., Luo, W., Zhang, H., Dan, B.B., Li, F., Energy Consumption Model and Its Simulation for Manufacturing and Remanufacturing Systems (2016) International Journal of Advanced Manufacturing Technology, 87 (5-8), pp. 1557-1569; Warsi, S.S., Jaffery, S.H.I., Ahmad, R., Khan, M., Agha, M.H., Ali, L., Development and Analysis of Energy Consumption Map for High-speed Machining of Al 6061-T6 Alloy (2018) International Journal of Advanced Manufacturing Technology, 96 (1-4), pp. 91-102; Yan, J.H., Li, L., Multi-objective Optimization of Milling Parameters-the Trade-offs between Energy, Production Rate and Cutting Quality (2013) Journal of Cleaner Production, 52, pp. 462-471; Yang, H.D., Li, H.C., Zhu, C.J., Fang, H., Li, J., A Process Parameters Selection Approach for Trade-off between Energy Consumption and Polishing Quality (2018) International Journal of Computer Integrated Manufacturing, 31 (4-5), pp. 380-395; Yoon, H.S., Lee, J.Y., Kim, M.S., Ahn, S.H., Empirical Power-consumption Model for Material Removal in Three-axis Milling (2014) Journal of Cleaner Production, 78 (78), pp. 54-62; Zhao, G.Y., Liu, Z.Y., He, Y., Cao, H.J., Guo, Y.B., Energy Consumption in Machining: Classification, Prediction, and Reduction Strategy (2017) Energy, 133, pp. 142-157; Zhou, L.R., Li, J.F., Li, F.Y., Meng, Q., Energy Consumption Model and Energy Efficiency of Machine Tools: A Comprehensive Literature Review (2016) Journal of Cleaner Production, 112 (5), pp. 3721-3734; Zhou, Y., Zhang, H., Yan, W., Ma, F., Li, G.F., Cheng, W.T., Energy Consumption Prediction Model of Plane Grinder Processing System Based on BP Neural Network (2018) International Journal of Wireless and Mobile Computing, 14 (4), pp. 320-327 [format_title_en_publication_en_pub_year] => 02a0c1204b92f65bc61e8f7f64e4f4f01574526208 [abstract_en] => The global energy crisis and climate warming are becoming more and more serious. How to reduce energy consumption and environmental pollution to achieve low-carbon manufacturing is an urgent problem to solve. Tool wear leads to the increase of cutting force and cutting power of machine tools obviously. So the influence of tool wear on energy consumption of machine tools cannot be ignored. Firstly, the power of CNC machine tool in cutting stage is divided into standby power, cutting material power and spindle no-load power in the paper. Then, the specific energy consumption prediction model of CNC machine tools based on tool wear is developed. Furthermore, the proposed model is verified with dry milling 45# steel experiments, and the prediction accuracy can reach 98.2% according to material removal rate, spindle speed and tool wear. Finally, the influence of processing parameters and tool wear on specific energy consumption of machine tools is studied. The research is helpful to optimize the processing parameters and tool conditions to reduce the energy consumption of machine tools. [scopus_id] => 55568077200;57214917959;57214220919;55716192300;36728757500; [from_id] => 76,74,73 [cauthor_ad] => [Zhao, GY]Shandong Univ Technol, Inst Adv Mfg, Zibo, Peoples R China. [hx_id] => 2376,2378,2371 [classification_pub] => ICIME [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [format_issn_issue_page_pub_year] => e210c5e9a43dad24697a16abe483925d841898760 [title_en] => Specific energy consumption prediction model of CNC machine tools based on tool wear [index_keyword] => Computer control systems; Cutting; Energy policy; Energy utilization; Forecasting; Machine tools; Wear of materials; Cutting power; Prediction accuracy; Processing parameters; Specific energy consumption; Tool wear; Cutting tools [volume] => 33 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Processing parameters; tool wear; cutting power; specific energy; consumption; prediction accuracy [article_id] => 814753,813826,808337 [begin_page] => 159 [hints] => 1 [publisher] => TAYLOR & FRANCIS LTD [doi] => 10.1080/0951192X.2020.1718763 [language] => English [issue] => 2 [issn] => 0951-192X [batch] => 3422,3418,3424 [publication_en] => INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING [email] => zgy709@126.com [sys_update_time] => 2020-03-13 09:56:31 [format_title_en_issn_pub_year] => 3f931ffddc56f27196d5ce4ee19da008-1530820555 [publication_iso] => Int. J. Comput. Integr. Manuf. [SYS_TAG] => 3 [end_page] => 168 [page] => 159-168 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Zhao, Guoyong; Li, Chunxiao; Lv, Zhe; Cheng, Xiang; Zheng, Guangming [ei_No] => 20200508100796 [eissn] => 1362-3052 [numerical_index] => Percentage 9.82e+01% [main_eword] => Cutting tools [format_publication_cn] => [format_title_en] => 9b4fc5eb9a7665f2c7e0cd7bdab9cc05-22468501 [pub_date] => FEB 1 [classification_No] => 525.3 Energy Utilization - 525.6 Energy Policy - 603.1 Machine Tools, General - 603.2 Machine Tool Accessories - 723.5 Computer Applications - 951 Materials Science [cauthor_order] => 1,1 [uncontrolled_terms] => Cutting power - Prediction accuracy - Processing parameters - Specific energy consumption - Tool wear [controlled_terms] => Computer control systems - Cutting - Energy policy - Energy utilization - Forecasting - Machine tools - Wear of materials [reference_No] => 27 [format_ei_No] => 9b7b829f0716767ed5cb6fce6a5f2807538982274 [sys_priority_field] => 73 [cauthor_back] => Zhao, Guoyong@@@Zhao, GY [format_publication_en] => 923a7b7592f3d4a4c618932b7d546582348550992 [cite_wos] => 0 [check_3Y] => 1 [delivery_No] => KH6DB [pages] => 10 [publication_29] => INT J COMPUT INTEG M [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => POWER-CONSUMPTION; MATERIAL REMOVAL; REQUIREMENTS; OPTIMIZATION; PARAMETERS; EFFICIENCY [fund_ab] => This work was supported by the Project of Shandong Province key research; development of China [2017GGX30114]; Project of Shandong Province; Natural Science Foundation of China [ZR2016EEM29]. [publisher_city] => ABINGDON [cite_awos] => 0 [wos_No] => WOS:000510739500004 [format_wos_No] => c332d478ab698a2799e261dfb88c6403-1026784000 [wos_sub] => Computer Science, Interdisciplinary Applications; Engineering,; Manufacturing; Operations Research & Management Science [research_area] => Computer Science; Engineering; Operations Research & Management Science [check_180] => 1 [publisher_ad] => 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => internationaljournalofcomputerintegratedmanufacturing [jl_country] => 中国 [jl_keyword_en] => consumption,specificenergy,cuttingpower,processingparameters,predictionaccuracy,toolwear [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => taylorandfrancisltd [company_id] => 0,0 [author_id] => [id] => 0AA103ABe-eYmRwwIxqf [tags] => 0 ) [8] => Array ( [standard_in] => Department of Engineering Center, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China; Science and Engineering College, Shandong University of Science and Technology, Qingdao, 266590, China; SenseTime Group Limited, Beijing, 100084, China [cauthor] => Wang, Yanhong(wangyanhong@fio.org.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85077808403 [author_in] => [Wang, Yanhong; Zhou, Xinghua; Chen, Yilan; Yang, Lei] Minist Nat Resources, Inst Oceanog 1, Dept Engn Ctr, Qingdao 266061, Shandong, Peoples R China.@@@ [Zhou, Xinghua] Shandong Univ Sci & Technol, Ocean Sci & Engn Coll, Qingdao 266590, Shandong, Peoples R China.@@@ [Li, Cong] SenseTime Grp Ltd, Beijing 100084, Peoples R China. [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077808403&doi=10.1109%2fLGRS.2019.2915122&partnerID=40&md5=4d94d336ff3580dceab53f49e5e9c3d9 [tag] => 0 [author_en] => Wang, YH; Zhou, XH; Li, C; Chen, YL; Yang, L [format_scopus_No] => 40b04a0c97dae34984f84266fde0b803-1189216585 [format_doi] => 7fddde27c550a4d23306ec702b9b292d140753872 [sys_update_time] => 2020-03-13 09:56:31 [fund_No] => National Key Research and Development Program of China [2017YFC0306003];; Satellite Remote Sensing Mapping Application [GW0219002]; National; Natural Science Foundation of ChinaNational Natural Science Foundation; of China [41806214] [reference] => Lyzenga, D.R., Passive remote sensing techniques for mapping water depth and bottom features (1978) Appl. Opt, 17 (3), pp. 379-383; Lyzenga, D.R., Malinas, N.P., Tanis, F.J., Multispectral bathymetry using a simple physically based algorithm (2006) IEEE Trans. Geosci. Remote Sens, 44 (8), pp. 2251-2259. , Aug; Liang, Z.-C., Study of the water depth retrieval based on artificial neural network (2012) Eng. Surveying Mapping, 21 (4), pp. 17-21; Stumpf, R.P., Holderied, K., Sinclair, M., Determination of water depth with high-resolution satellite imagery over variable bottom types (2003) Limnology Oceanogr, 48 (1), pp. 547-556; Hinton, G.E., Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks (2006) Science, 313 (5786), pp. 504-507; Lecun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521, pp. 436-444. , May; Mnih, V., Hinton, G.E., Learning to detect roads in high-resolution aerial images (2010) Proc. Eur. Conf. Comput. Vis, 6316 (5), pp. 210-223; Zhan, Y., Wang, J., Shi, J., Cheng, G., Yao, L., Sun, W., Distinguishing cloud and snow in satellite images via deep convolutional network IEEE Geosci. Remote Sens. Lett, 14 (10), pp. 1785-1789. , Oct. 2017; Liu, D.W., Han, L., Han, X.Y., High spatial resolution remote sensing image classification based on deep learning (2016) Acta Optica Sinica, 36 (4). , Art. no 0428001; Wang, L., Zhang, J., Liu, P., Choo, K.-K.R., Huang, F., Spectral-spatial multi-feature-based deep learning for hyperspectral remote sensing image classification (2017) Soft Comput, 21 (1), pp. 213-221; Zhang, H., Liu, X.Y., Yang, S., Yu, L.I., Retrieval of remote sensing images based on semisupervised deep learning (2017) J. Remote Sens, 21 (3), pp. 406-414; Luus, F.P.S., Salmon, B.P., Van Den Bergh, F., Maharaj, B.T.J., Multiview deep learning for land-use classification (2015) IEEE Geosci. Remote Sens. Lett, 12 (12), pp. 2448-2452. , Dec; Ruder, S., (2016) An Overview of Gradient Descent Optimization Algorithms, , https://arxiv.org/abs/1609.04747, Sep; Yanhong, W., Yilan, C., Xinghua, Z., Lei, Y., Yanguang, F., Research on reef bathymetry using remote sensing based on polynomial regression model (2018) Acta Oceanologica Sinica, 40 (3), pp. 121-128; Su, H., Liu, H., Wang, L., Filippi, A.M., Heyman, W.D., Beck, R.A., Geographically adaptive inversion model for improving bathymetric retrieval from satellite multispectral imagery (2014) IEEE Trans. Geosci. Remote Sens, 52 (1), pp. 465-476. , Jan [format_title_en_publication_en_pub_year] => 68621a1318b2c21026b534e2d4b54dff1702651514 [abstract_en] => Multispectral methods for remote sensing image have been widely applied to shallow water bathymetry by researchers. In nonideal conditions, even with the same spectral radiance, the points still have a very wide range of water depths. This means that spectral features alone are insufficient for water bathymetry. Hence, we need to extract other valuable features from a remote sensing image. This letter introduces a spatial feature for water bathymetry using remote sensing images. We propose a model that utilizes a multilayer perceptron (MLP) to integrate the spectral and spatial location features. Experimental results demonstrate that the proposed model yields a substantial performance improvement. The mean relative error is only 8.41, and the root mean square error is reduced by 34-68 when compared with three other models. Furthermore, the proposed model addresses well the problems caused by heterogeneous bottom types. [scopus_id] => 57200063977;57198478670;55794405500;16505913000;57213456832; [from_id] => 76,74,73 [cauthor_ad] => [Wang, YH]Minist Nat Resources, Inst Oceanog 1, Dept Engn Ctr, Qingdao 266061, Shandong, Peoples R China. [hx_id] => 2376,2378,2371 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [format_issn_issue_page_pub_year] => 101e918436d50df97aba040aba1a1f5b-1741446078 [title_en] => Bathymetry Model Based on Spectral and Spatial Multifeatures of Remote Sensing Image [index_keyword] => Bathymetry; Mean square error; Multilayers; Mean relative error; Multi layer perceptron; Multiple features; Non-ideal conditions; Remote sensing images; Root mean square errors; Shallow water bathymetry; Spectral radiance; Remote sensing; bathymetry; experimental study; image analysis; numerical model; remote sensing; satellite imagery; shallow water; spatial analysis; spectral analysis; water depth [volume] => 17 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Remote sensing; Training; Sea measurements; Neural networks; Feature; extraction; Machine learning algorithms; Deep learning; Bathymetry;; multilayer perceptron (MLP); multiple features; remote sensing [article_id] => 809632,812839,814093 [begin_page] => 37 [hints] => 1 [publisher] => IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC [doi] => 10.1109/LGRS.2019.2915122 [language] => English [issue] => 1 [issn] => 1545-598X [batch] => 3422,3418,3424 [publication_en] => IEEE GEOSCIENCE AND REMOTE SENSING LETTERS [email] => wangyanhong@fio.org.cn; zhouxh@fio.org.cn; licong1018@163.com;; chenyilan@fio.org.cn; yanglei@fio.org.cn [document_No] => 8732587 [format_title_en_issn_pub_year] => c9e5284ff63cb3e264169e35510d8cb4648542030 [publication_iso] => IEEE Geosci. Remote Sens. Lett. [SYS_TAG] => 3 [end_page] => 41 [page] => 37-41 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Wang, Yanhong; Zhou, Xinghua; Li, Cong; Chen, Yilan; Yang, Lei [ei_No] => 20200308044843 [eissn] => 1558-0571 [numerical_index] => Percentage 3.40e+01% to 6.80e+01%, Percentage 8.41e+00% [main_eword] => Remote sensing [format_publication_cn] => [format_title_en] => 26658b675a399897b9d02249ead0dd8e-606547741 [pub_date] => JAN [classification_No] => 471.3 Oceanographic Techniques - 922.2 Mathematical Statistics [cauthor_order] => 1,1 [uncontrolled_terms] => Mean relative error - Multi layer perceptron - Multiple features - Non-ideal conditions - Remote sensing images - Root mean square errors - Shallow water bathymetry - Spectral radiance [controlled_terms] => Bathymetry - Mean square error - Multilayers [reference_No] => 15 [format_ei_No] => 45fcaf5d9a8887b63e58df2247ba62cf740057541 [sys_priority_field] => 73 [cauthor_back] => Wang, Yanhong@@@Wang, YH [format_publication_en] => bc3ff28f4c5c2b1ea2a85fdfd3dc099a-165322449 [cite_wos] => 0 [check_3Y] => 3 [delivery_No] => KA0ZF [pages] => 5 [publication_29] => IEEE GEOSCI REMOTE S [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => WATER DEPTH [fund_ab] => This work was supported in part by the National Key Research and; Development Program of China under Grant 2017YFC0306003, in part by the; Satellite Remote Sensing Mapping Application under Grant GW0219002, and; in part by the National Natural Science Foundation of China under Grant; 41806214. [publisher_city] => PISCATAWAY [cite_awos] => 0 [wos_No] => WOS:000505528400008 [format_wos_No] => a6223a7703ccc3d0c1ab914979d35a261672097190 [wos_sub] => Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote; Sensing; Imaging Science & Photographic Technology [research_area] => Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science; & Photographic Technology [check_180] => 3 [publisher_ad] => 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => ieeegeoscienceandremotesensingletters [jl_country] => 中国 [jl_keyword_en] => ,training,multilayerperceptronmlp,neuralnetworks,multiplefeatures,seameasurements,feature,remotesensing,extraction,machinelearningalgorithms,deeplearning,bathymetry [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => ieeeinstelectricalelectronicsengineersinc [company_id] => 0,0 [author_id] => [id] => PQA103ABe-eYmRwwIRg7 [tags] => 0 ) [9] => Array ( [standard_in] => School of Control Science and Engineering, Shandong University, Jinan, 250061, China [cauthor] => Zang, Li-Lin(llzang@sdu.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85072551408 [author_in] => [Song Ze-Rui; Zang Li-Lin; Zhu Wen-Xing] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China. [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072551408&doi=10.1016%2fj.physa.2019.122691&partnerID=40&md5=530256147d399ab6ef765df1c3092ab1 [tag] => 0 [author_en] => Song, ZR; Zang, LL; Zhu, WX [format_scopus_No] => a7d5e980a31eae77aac27b6e7d095c451971444668 [format_doi] => d71f948a00ece727aa4f1249cdae6c6d1721291773 [sys_update_time] => 2020-03-13 09:56:09 [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [61773243]; Natural Science Foundation of Shandong; Province, ChinaNatural Science Foundation of Shandong Province; [ZR2017MF011] [reference] => Samet, J., Krewski, D., (2007) J. Toxicol. Environ. Health A, 70, p. 227; Cao, J., Yang, C.X., Li, J.X., Chen, R.J., Chen, B.H., Gu, D.F., Kan, H.D., (2011) J. Hard Mater., 186, p. 1594; Chan, C.K., Yao, X.H., (2008) Atmos. Environ., 42, p. 1; Lee, H.J., Coull, B.A., Bell, M.L., Koutrakis, P., (2012) Environ. Res., 118, p. 8; Zou, B., Peng, F., Wan, N., Mamady, K., Wilson, G.J., (2014) PLoS One, 9; Beevers, S.D., Kitwiroon, N., Williams, M.L., Kelly, F.J., Anderson, H.R., Carslaw, D.C., (2013) J. Expo. Sci. Environ. Epidemiol., 23, p. 647; Júnior, W.J.R., Roig, H.L., Koutrakis, P., (2015) Environ. Int., 85, p. 334; Oliveira, A.D., Ignotti, E., Artaxo, P., Saldiva, D.N., Junger, W.L., Hacon, S., (2012) Environ. Health, 11, p. 64; Requia, W.J., Roig, H.L., Koutrakis, P., Adams, M.D., (2017) J. Cleaner Prod., 148, p. 845; Costa, L.G., Cole, T.B., Coburn, J., Chang, Y.C., Dao, K., Roqué, P.J., (2015) Neurotoxicology, 59, p. 133; Pipes, L.A., (1953) J. Appl. Phys., 24, p. 274; Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y., (1995) Phys. Rev. E, 51, p. 1035; Helbing, D., Tilch, B., (1998) Phys. Rev. E, 58, p. 133; Sasaki, M., Nagatani, T., (2003) Physica A, 325, p. 531; Zhu, W.X., Zhang, L.D., (2014) Internat. J. Modern Phys. C, 25; Yu, S.W., Shi, Z.K., (2014) Physica A, 407, p. 152; Nagatani, T., (2012) Physica A, 387, p. 1637; Yu, S.W., Fu, R., Guo, Y.S., Xin, Q., Shi, Z.K., (2019) Physica A, 531; Xin, Q., Yang, N., Fu, R., Yu, S.W., Shi, Z.K., (2018) Physica A, 501, p. 338; Tang, T.Q., Huang, H.J., Shang, H.Y., (2017) Physica A, 468, p. 322; Tang, T.Q., Huang, H.J., Shang, H.Y., (2015) Transp. Res. D, 41, p. 423; Wu, X., Zhao, X.M., Song, H.S., Xin, Q., Yu, S.W., (2019) Physica A, 515, p. 192; Xin, Q., Fu, R., Yuan, W., Liu, Q.L., Yu, S.W., (2018) Physica A, 508, p. 806; Tang, T.Q., Huang, H.J., Shang, H.Y., (2017) Physica A, 468, p. 322; Ou, H., Tang, T.Q., Zhang, J., Zhou, J.M., (2018) Phys. Lett. A, 382, p. 2819; Zhang, J., Tang, T.Q., Yu, S.W., (2018) Physica A, 492, p. 1831; Liao, P., Tang, T.Q., Wang, T., Zhang, J., (2019) Physica A, 525, p. 108; Tang, T.Q., Shi, W.F., Huang, H.J., Wu, W.X., Song, Z.Q., (2019) Physica A, 514, p. 767; Smit, R., Ntziachristos, L., Boulter, P., (2010) Atmos. Environ., 44, p. 2943; Zegeye, S.K., Schutter, B.D., Hellendoorn, H., Breunesse, E., (2009) IFAC Proc. Vol., 42, p. 149; Chen, K., Yu, L., (2007) J. Transp. Syst. Eng. Inf. Technol., 7, p. 93; Smit, R., Casas, J., Torday, A., (2013), in: Australasian Transport Research Forum 2013 Proceedings, Brisbane; Stevanovic, A., Stevanovic, J., Zhang, K., Batterman, S., (2009) Transp. Res. Rec., 2128, p. 105; Ghafghazi, G., Hatzopoulou, M., (2014) Transportation, 41, p. 633; Zhu, W.X., Zhang, J.Y., (2017) Physica A, 467, p. 107; Zhu, W.X., (2013) Physica A, 392, p. 4787; Tang, X.N., (2014), (Dissertation for Master Degree), Beijing Jiaotong University, Beijing; Samaras, C., Tsokolis, D., Toffolo, S., Magra, G., Ntziachristos, L., Samaras, Z., (2018) Transp. Res. D, 65, p. 772; Yu, S.W., Fu, R., Guo, Y.S., Xin, Q., Shi, Z.K., (2018) PLoS One, 13; Sun, Y.Q., Ge, H.X., Cheng, R.J., (2019) Physica A, 521, p. 752; Cheng, R.J., Ge, H.X., Wang, J.F., (2018) Appl. Math. Comput., 332, p. 493; Cheng, R.J., Wang, Y.N., (2019) Physica A, 513, p. 510; Jiang, C.T., Ge, H.X., Cheng, R.J., (2019) Physica A, 513, p. 465; Cariou, P., Cheaitou, A., Larbi, R., Hamdan, S., (2018) Transp. Res. D, 63, p. 604; Nesmachnow, S., Massobrio, R., Arreche, E., Mumford, C., Olivera, A.C., Vidal, P.J., Tchernykh, A., (2019) Int. J. Transp. Sci. Technol., 8, p. 53; Guo, D., J.Wang, R., Zhao, J.B., Sun, F., Gao, S., Li, C.D., Li, M.H., Li, C.C., (2019) Sci. Total Environ., 663, p. 935; Lee, J., Abdulhai, B., Shalaby, A., Chuang, E.H., (2005) J. Intell. Transp. Syst., 9, p. 111; Jiménez, P., Luis, J., (1999), (Dissertation for Doctor Degree), Massachusetts Institute of Technology, Boston; Liu, J.J., (2010), (Dissertation for Master Degree), Beijing Jiaotong University, Beijing; Tang, P.J., (2013), (Dissertation for Master Degree), Beijing Jiaotong University, Beijing [format_title_en_publication_en_pub_year] => 1e1f6f21c79087f30c6cc8af75d5ad3e-645029747 [abstract_en] => In this paper, an optimal signal control model was presented to minimize the traffic emissions on an arterial road. In order to solve the optimal model, an improved simulated annealing genetic algorithm (ISAGA) was utilized by integrating microscopic traffic flow model with vehicle emission model. For simplicity, three intersections on a signalized arterial road were taken into account. During the optimizing process, the traffic flow model and the emission model were embedded into ISAGA as a fitness calculating module. Through the experimental simulation, the results indicate that the optimal signal timing of the multi-intersection was realized by minimizing the emissions of vehicles on the arterial road. Moreover, it is found that the improved simulated annealing genetic algorithm is effective in solving the optimization model. (C) 2019 Published by Elsevier B.V. [scopus_id] => 57208407329;15129173900;24537796000; [from_id] => 76,74,73 [cauthor_ad] => [Zang, LL; Zhu, WX]Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China. [hx_id] => 2376,2378,2371 [classification_pub] => PHYAD [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [title_en] => Study on minimum emission control strategy on arterial road based on improved simulated annealing genetic algorithm [index_keyword] => Emission control; Genetic algorithms; Roads and streets; Simulated annealing; Traffic signals; Vehicles; Arterial roads; Signal control; Simulated annealing-genetic algorithms; Traffic flow modeling; Vehicle emission models; Street traffic control [volume] => 537 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Microscopic traffic flow model; Vehicle emission model; Signal control;; Arterial road; Improved simulated annealing genetic algorithm [article_id] => 812499,819378,809507 [hints] => 1 [publisher] => ELSEVIER [doi] => 10.1016/j.physa.2019.122691 [language] => English [issn] => 0378-4371 [batch] => 3422,3418,3424 [publication_en] => PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS [email] => llzang@sdu.edu.cn; zhuwenxing@sdu.edu.cn [document_No] => 122691 [format_title_en_issn_pub_year] => 9a75b96809459b31660832465e7839ce1538096437 [publication_iso] => Physica A [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Song Ze-Rui; Zang Li-Lin; Zhu Wen-Xing [ei_No] => 20193907473158 [main_eword] => Street traffic control [format_publication_cn] => [format_title_en] => 355567e5aa585e3d665c678515b75acd2025305834 [pub_date] => JAN 1 [classification_No] => 406.2 Roads and Streets - 432.4 Highway Traffic Control - 451.2 Air Pollution Control - 537.1 Heat Treatment Processes [cauthor_order] => 2,2,3 [uncontrolled_terms] => Arterial roads - Signal control - Simulated annealing-genetic algorithms - Traffic flow modeling - Vehicle emission models [controlled_terms] => Emission control - Genetic algorithms - Roads and streets - Simulated annealing - Traffic signals - Vehicles [reference_No] => 49 [format_ei_No] => 606b6fd375403a7ccf90317b86e4285c1751130109 [sys_priority_field] => 73 [cauthor_back] => Zang, LiLin@@@Zang, LL@@@Zhu, WX [format_publication_en] => 9b2f32127bc069a10f4552da1c9e9c641042358383 [cite_wos] => 0 [check_3Y] => 6 [delivery_No] => JU4IL [pages] => 11 [publication_29] => PHYSICA A [eissn] => 1873-2119 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => CAR-FOLLOWING MODEL; TRAFFIC FLOW MODEL; DRIVERS BOUNDED RATIONALITY; FUEL CONSUMPTION; AIR-POLLUTION; EXPOSURE; NETWORK [fund_ab] => This work is partially supported by the National Natural Science; Foundation of China (Grant No. 61773243), and Natural Science Foundation; of Shandong Province, China (Grant No. ZR2017MF011). [publisher_city] => AMSTERDAM [cite_awos] => 0 [wos_No] => WOS:000501641200069 [format_wos_No] => 78c10d7f586a3657ab69fd60cf1d52d6-146831040 [wos_sub] => Physics, Multidisciplinary [research_area] => Physics [check_180] => 6 [publisher_ad] => RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => physicaastatisticalmechanicsanditsapplications [jl_country] => 中国 [jl_keyword_en] => vehicleemissionmodel,,microscopictrafficflowmodel,improvedsimulatedannealinggeneticalgorithm,signalcontrol,arterialroad [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elsevier [author_test] => Array ( [0] => Array ( [sure] => 1 [irmagnum] => 0 [u_index] => 0 [name] => 朱文兴 [sys_author_id] => Array ( [0] => 26204 ) [irtag] => 0 [t_index] => 0 [person_id] => 26204 ) ) [company_id] => 0,144 [author_id] => 26204 [sys_subject_sort] => 0 [college_parent_id] => 144 [company_test] => Array [id] => GQA003ABe-eYmRww6A0C [tags] => 0 ) [10] => Array ( [cite_scopus] => 3 [cauthor] => Peng, Yong(yong_peng@csu.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85078708075 [author_in] => [Zhang, Honghao; Peng, Yong; Hou, Lin] Cent S Univ, Minist Educ, Key Lab Traff Safety Track, Sch Traff & Transportat Engn, Changsha 410000, Peoples R China.@@@ [Wang, Danqi] Jilin Univ, Coll Automot Engn, Changchun 130025, Peoples R China.@@@ [Tian, Guangdong] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China.@@@ [Li, Zhiwu] Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China.@@@ [Li, Zhiwu] Xidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China. [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078708075&doi=10.1109%2fTII.2019.2936048&partnerID=40&md5=ab1258a5ff09adc4a2e681cb65d3a5b3 [tag] => 0 [author_en] => Zhang, HH; Peng, Y; Hou, L; Wang, DQ; Tian, GD; Li, ZW [format_scopus_No] => f1e2c480651cd87ced2250a050813e18522758053 [format_doi] => 1ce75d05fc4ceaf7438540c6474a8788537438260 [sys_update_time] => 2020-03-13 09:56:30 [fund_No] => National Key Research and Development Programof China [2016YFB1200505];; National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [51775238]; Fundamental Research Funds for the; Central Universities of Central South University [2018zzts159]; Natural; Science Foundation of HunanNatural Science Foundation of Hunan Province; [2015JJ3155]; Innovation-Driven Project of Central South University; [2018CX021]; Hu-Xiang Youth Talent Program [2018RS3002] [reference] => Hu, H., Tang, B., Gong, X.J., Wei, W., Wang, H.H., Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks (2017) IEEE Trans. Ind. Informat., 13 (4), pp. 2106-2116. , Aug; (2018) International Union of Railways (UIC), UIC Safety Rep., , https://uic.org/IMG/pdf/sdb-report-2018-public.pdf, [Online]. Available, 2017-10/2018-2; Xie, S.C., Li, H.H., Yang, C.X., Yao, S.G., Crashworthiness optimisation of a composite energy-absorbing structure for subway vehicles based on hybrid particle swarm optimisation (2018) Struct. Multidisciplinary Optim., 58 (5), pp. 2291-2308; Estrada, Q., Effect of radial clearance and holes as crush initiators on the crashworthiness performance of bi-tubular profiles (2019) Thin Walled Struct., 140, pp. 43-59; (2012) Railway Safety Requirements for Rail Vehicle Structures, , Railway Group Standard: GM/RT 2100; Yao, S.G., Yan, K.B., Lu, S.S., Xu, P., Energy-absorption optimisation of locomotives and scaled equivalent model validation (2017) Int. J. Crashwor-thines, 22 (4), pp. 1-12; Tyrell, D.C., US rail equipment crashworthiness standards (2002) Proc. Inst. Mech. Eng. F, J. Rail, 216 (2), pp. 123-130; (2008) Railway Applications: Crashworthiness Requirements for Railway Vehicle Bodies, , BS EN 15227-2008; Zhang, H.H., Peng, Y., Hou, L., Tian, G.D., Li, Z.W., A hybrid multi-objective optimization approach for energy-absorbing structures in train collisions (2019) Inf. Sci., 481, pp. 491-506; Tyrell, D.C., Passenger rail train-to-train impact test volume I: Overview and selected results (2003) Federal Railroad Administration, Office of Research and Development, USA; Zhao, L., Zhang, Z., Wang, S., Zhang, H.W., Numerical simulation of locomotive collision in different working conditions (2014) J. Mech. Res. Appl., 27 (6), pp. 91-94; Drazetic, P., Level, P., Cornette, D., Mongenie, P., Ravalard, Y., One-dimensional modelling of contact impact problem in guided transport vehicle crash (1995) Int. J. Impact Eng., 16 (3), pp. 467-478; Xie, Z.J., Xu, P., Luo, Y.Q., Optimization for crashworthiness ofurban transit trains using genetic algorithm (2011) Appl. Mech. Mater., 66-68, pp. 1167-1172; Singh, U., Singh, S.N., Optimal feature selection via NSGA-II for power quality disturbances classification (2018) IEEE Trans. Ind. Informat., 14 (7), pp. 2994-3002. , Jul; Pham, M.T., Zhang, D., Koh, C.S., Multi-guider and cross-searching approach in multi-objective particle swarm optimization for electromagnetic problems (2012) IEEE Trans. Magn., 48 (2), pp. 539-542. , Feb; Afshar, A., Sharifi, F., Jalali, M.R., Non-dominated archiving multi-colony ant algorithm for multi-objective optimization: Application to multi-purpose reservoir operation (2009) Eng. Optim., 41 (4), pp. 313-325; Gao, H., Shi, Y., Pun, C.M., Kwong, S., An improved artificial bee colony algorithm with its application (2019) IEEE Trans. Ind. Informat., 15 (4), pp. 1853-1865. , Apr; Wu, Y., Chen, K., Zeng, B., Yang, M., Li, L., Zhang, H., A cloud decision framework inpure 2-tuple linguistic setting and its application for lowspeed wind farm site selection (2017) J. Cleaner Prod., 142, pp. 2154-2165; Shi, H., Liu, H.C., Li, P., Xu, X.G., An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder (2017) Waste Manage., 59, pp. 508-517; Tyrell, D.C., US rail equipment crashworthiness standards (2001) Proc. Inst. Mech. Eng. F, J. Rail, 216 (2), pp. 123-130; Hou, W.X., Development of european railway collision technology (1998) J. Foreign Railway Veh., 35 (1), pp. 23-26; Ling, L., Xiao, X.B., Jin, X.S., Development of a simulation model for dynamic derailment analysis of high-speed trains (2014) Acta Mech. Sin., 30 (6), pp. 860-875; Zhai, W., Wang, K., Cai, C., Fundamentals of vehicle-track coupled dynamics (2009) Veh. Syst. Dyn., 47 (11), pp. 1349-1376; Marquis, B., Pascal, J.P., Report on a railway benchmark simulating a single wheelset without friction impacting a rigid track (2008) Veh. Syst. Dyn., 46 (1-2), pp. 93-116; Ling, L., Guan, Q., Dhanasekar, M., Thambiratnam, D.P., Dynamic simulation of train-truck collision at level crossings (2017) Veh. Syst. Dyn., 55 (1), pp. 1-22; Paul, A.K., Shill, P.C., New automatic fuzzy relational clustering algorithms using multi-objective NSGA-II (2018) Inf. Sci., 448, pp. 112-133; Kumar, M., Guria, C., The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization (2017) Inf. Sci., 382, pp. 15-37. , Mar; Ahmadi, M.H., Ahmadi, M.A., Bayat, R., Ashouri, M., Feidt, F., Thermo-economic optimization of stirling heat pump by using non-dominated sorting genetic algorithm (2015) Energy Convers. Manage., 91, pp. 315-322; Zhou, G.H., Lu, Q., Xiao, Z.D., Zhou, C., Tian, C.L., Cutting parameter optimization for machining operations considering carbon emissions (2019) J. 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Soft Comput., 59, pp. 316-325; Guo, S., Zhao, H., Fuzzy best-worst multi-criteria decision-making method and its applications (2017) Knowl.-Based Syst., 121, pp. 23-31; Li, D.Y., Liu, C.Y., Gan, W.Y., A new cognitive model: Cloud model (2009) Int. J. Intell. Syst., 24 (3), pp. 357-375; Wang, J.Q., Peng, J.J., Zhang, H.Y., Liu, T., Chen, X.H., An uncertain linguistic multi-criteria group decision-making method based on a cloud model (2015) Group Decis. Negotiation, 24 (1), pp. 171-192; Liu, H.C., Luan, X., Li, Z.W., Wu, J.N., Linguistic Petri nets based on cloud model theory for knowledge representation and reasoning (2018) IEEE Trans. Knowl. Data Eng., 30 (4), pp. 717-728. , Apr; Song, B.C., Chen, S.L., Cui, T.J., Research on the choice scheme of the subway tunnel construction way based on DAHP-ECM (2016) Math. Practice Theory, 46 (21), pp. 112-119; Tian, G.D., Operation patterns analysis of automotive components remanufacturing industry development in China (2017) J. Cleaner Prod., 164, pp. 1363-1375; Jin, X., Xiao, X., Ling, L., Zhou, L., Xiong, J., Study on safety boundary for high-speed train running in severe environments (2013) Int. J. Rail Transportation, 1 (1-2), pp. 87-108; (2002) AV/ST9001: Vehicle Interior Crashworthiness, , London U.K.: Assoc. Train Operating Companies; Yang, C.X., Li, Q.M., Further assessment of deceleration-time histories for occupant injury and the damage of protected object in a crash stop (2019) Int. J. Impact Eng., 130, pp. 184-191; (1996) 49 CFR Part 238 Passenger Equipment Safety Standards; Proposed Rule, , U.S. Department of Transportation, Federal Railroad Administration, Washington, DC, USA; Kang, Q., Song, X.Y., Zhou, M.C., Li, L., A collaborative resource allocation strategy for decomposition-based multiobjective evolutionary algorithms IEEE Trans. Syst. Man Cybern., Syst, , to be published; Du, W., Zhong, W., Tang, Y., Du, W.L., Jin, Y.C., High-dimensional robust multi-objective optimization for order scheduling: A decision variable classification approach (2019) IEEE Trans. Ind. Informat., 15 (1), pp. 293-304. , Jan; Wang, B., Xia, X.D., Meng, H.X., Li, T., Bad-scenario-set robust optimization framework with two objectives for uncertain scheduling systems (2017) IEEE/CAA J. Autom. Sin., 4 (1), pp. 143-153. , Jan [format_title_en_publication_en_pub_year] => 1e558f6f2ceef41ca6d4004f023719c347277792 [abstract_en] => With the increasing speed of railway vehicles, deciding how to reasonably distribute impact energy to each vehicle has been a widespread concern in safety protection systems. This article formulates a three-dimensional train-track coupling dynamics model using MAthematical DYnamic MOdels (MADYMO) multibody dynamics software. A train-to-train collision is then simulated using this model. A hybrid solution methodology that combines the non-dominated sorting genetic algorithm II (NSGA-II), modified best and worst method with cloud model theory and grey relational analysis is proposed. The optimization parameters and objectives are determined based on the EN15227 crashworthiness requirements for railway vehicles. An empirical case of an existing train with eight vehicles that have been in operation in China is applied to verify this dynamics model derived from a high-speed train and solution methodology. Analysis and discussion are conducted to monitor the robustness of the results and the practical implications for rail transportation are summarized. The results prove that the obtained optimal solution by this research has better crashworthiness than an existing solution. [scopus_id] => 57190024208;56714313900;57195600952;57194212214;36619290600;57196398138; [from_id] => 76,74,73 [cauthor_ad] => [Peng, Y; Hou, L]Cent S Univ, Minist Educ, Key Lab Traff Safety Track, Sch Traff & Transportat Engn, Changsha 410000, Peoples R China. [hx_id] => 2376,2378,2371 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11,8,3 [format_issn_issue_page_pub_year] => a8e8b38a6534f4c7aa562a5a04d5f0821668750556 [title_en] => Multistage Impact Energy Distribution for Whole Vehicles in High-Speed Train Collisions: Modeling and Solution Methodology [index_keyword] => Accidents; Cloud computing; Dynamic models; Dynamics; Genetic algorithms; Locomotives; Optimization; Railroad cars; Railroad transportation; Railroads; Rails; Cloud model theories; Grey relational analysis; High speed train (HST); Hybrid solution methodology; Impact energy; Non dominated sorting genetic algorithm ii (NSGA II); Optimization parameter; Solution methodology; Crashworthiness [standard_in] => Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, 410000, China; College of Automotive Engineering, Jilin University, Changchun, 130025, China; School of Mechanical Engineering, Shandong University, Jinan, 250061, China; Institute of Systems Engineering, Macau University of Science and Technology999078, Macau; School of Electro-Mechanical Engineering, Xidian University, Xian, 710071, China [volume] => 16 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Vehicle dynamics; Solid modeling; Accidents; Couplers; Rail; transportation; Safety; Optimization; Cloud model theory; high-speed; train collisions; impact energy distribution; optimization [article_id] => 810752,815167,807848 [begin_page] => 2486 [hints] => 1 [publisher] => IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC [doi] => 10.1109/TII.2019.2936048 [language] => English [issue] => 4 [issn] => 1551-3203 [batch] => 3422,3418,3424 [publication_en] => IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS [email] => zhanghh2016@csu.edu.cn; yong_peng@csu.edu.cn; houlin@csu.edu.cn;; wangdq123321@163.com; tiangd2013@163.com; zhwli@xidian.edu.cn [document_No] => 8805408 [format_title_en_issn_pub_year] => eabf96e9a2564b8189bff25629bff658-565240715 [publication_iso] => IEEE Trans. Ind. Inform. [SYS_TAG] => 3 [end_page] => 2499 [page] => 2486-2499 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Zhang, Honghao; Peng, Yong; Hou, Lin; Wang, Danqi; Tian, Guangdong; Li, Zhiwu [ei_No] => 20200508115680 [eissn] => 1941-0050 [main_eword] => Crashworthiness [format_publication_cn] => [format_title_en] => 01c834078950ce0d356f65cb1ab592b4-1195923844 [pub_date] => APR [classification_No] => 433.1 Railroad Transportation, General - 681.1 Railway Plant and Structures, General - 682.1.1 Railroad Cars - 682.1.2 Locomotives - 722.4 Digital Computers and Systems - 914.1 Accidents and Accident Prevention - 921 Mathematics - 921.5 Optimization Techniques [cauthor_order] => 2,2,3 [uncontrolled_terms] => Cloud model theories - Grey relational analysis - High speed train (HST) - Hybrid solution methodology - Impact energy - Non dominated sorting genetic algorithm ii (NSGA II) - Optimization parameter - Solution methodology [controlled_terms] => Accidents - Cloud computing - Dynamic models - Dynamics - Genetic algorithms - Locomotives - Optimization - Railroad cars - Railroad transportation - Railroads - Rails [reference_No] => 45 [format_ei_No] => d1b633a6f79a7624bedff41925dbfcd4-618393533 [sys_priority_field] => 73 [cauthor_back] => Peng, Yong@@@Peng, Y@@@Hou, L [format_publication_en] => ef938d83cd56b4df991dfd45dae90958-177649701 [cite_wos] => 2 [check_3Y] => 10 [delivery_No] => KH8LB [pages] => 14 [publication_29] => IEEE T IND INFORM [researcherID] => ; Li, Zhiwu/A-7884-2010 [orcID] => PENG, Yong/0000-0003-0101-0342; Li, Zhiwu/0000-0003-1547-5503; Hou,; Lin/0000-0002-5577-0649 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => CRASHWORTHINESS OPTIMIZATION; NSGA-II; ALGORITHM; SELECTION [fund_ab] => This work was supported in part by the National Key Research and; Development Programof China under Grant 2016YFB1200505, in part by the; National Natural Science Foundation of China under 51775238, in part by; the Fundamental Research Funds for the Central Universities of Central; South University under Grant 2018zzts159, in part by the Natural Science; Foundation of Hunan under Grant 2015JJ3155, in part by the; Innovation-Driven Project of Central South University under Grant; 2018CX021, and in part by the Hu-Xiang Youth Talent Program under Grant; 2018RS3002. Paper no. TII-19-2442. [publisher_city] => PISCATAWAY [cite_awos] => 2 [wos_No] => WOS:000510901000030 [format_wos_No] => 97c42f2b3b32ef476bd2361b6fa5920a-874311297 [wos_sub] => Automation & Control Systems; Computer Science, Interdisciplinary; Applications; Engineering, Industrial [research_area] => Automation & Control Systems; Computer Science; Engineering [check_180] => 10 [publisher_ad] => 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => ieeetransactionsonindustrialinformatics [jl_country] => 中国 [jl_keyword_en] => transportation,cloudmodeltheory,impactenergydistribution,rail,vehicledynamics,solidmodeling,optimization,safety,couplers,highspeed,accidents,traincollisions [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => ieeeinstelectricalelectronicsengineersinc [company_id] => 0,130 [author_id] => [sys_subject_sort] => 0 [college_parent_id] => 130 [company_test] => Array [id] => bgA103ABe-eYmRwwJhwm [tags] => 0 ) [11] => Array ( [standard_in] => School of Civil and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Key Laboratory of High-Efficient Mining and Safety of Metal Mines, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; School of Resource and Environmental Engineering, ShanDong University of Technology, ZiBo, 255049, China [cauthor] => Wu, Aixiang(wuaixiang@xs.ustb.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85075475035 [author_in] => [Lan, Wentao; Wu, Aixiang] Univ Sci & Technol Beijing, Sch Civil & Environm Engn, Beijing 100083, Peoples R China.@@@ [Lan, Wentao; Wu, Aixiang] Univ Sci & Technol Beijing, Minist Educ, Key Lab High Efficient Min & Safety Met Mines, Beijing 100083, Peoples R China.@@@ [Yu, Ping] ShanDong Univ Technol, Sch Resource & Environm Engn, Zibo 255049, Peoples R China. [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075475035&doi=10.1016%2fj.jclepro.2019.119060&partnerID=40&md5=e8484587ee3219a54a456ccbe1466328 [tag] => 0 [author_en] => Lan, WT; Wu, AX; Yu, P [format_scopus_No] => 5bdd11d7e3ff9c18c8ca701461e4602a-598903550 [format_doi] => 0bc44a8ceacfdbd7a934451d83bc70bf79072961 [sys_update_time] => 2020-03-13 09:56:31 [fund_No] => National Key R&D Program of China [2017YFC0602903]; Key Program of; National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [51734001]; National Science Fund for Excellent; Young Scholars [51722401] [reference] => Arezoumandi, M., Volz, J.S., Effect of fly ash replacement level on the shear strength of high-volume fly ash concrete beams (2013) J. Clean. Prod., 59, pp. 120-130; Byrd, R.H., Gilbert, J.C., Nocedal, J., A trust region method based on interior point techniques for nonlinear programming (2000) Math. Program., 89, pp. 149-185; Cihangir, F., Ercikdi, B., Kesimal, A., Deveci, H., Erdemir, F., Paste backfill of high-sulphide mill tailings using alkali-activated blast furnace slag: effect of activator nature, concentration and slag properties (2015) Miner. Eng., 83, pp. 117-127; Deng, Y., Zhang, C., Wei, X., Influence of lithium sulfate addition on the properties of Portland cement paste (2014) Constr. Build. Mater., 50, pp. 457-462; Deng, Z., Zhou, W., Yue, H., Guo, X., Study on the hydration mechanism of a hardened slag-based plugging agent activated by alkalis (2019) Constr. Build. Mater., 203, pp. 343-355; Feng, Y., Yang, Q., Chen, Q., Kero, J., Andersson, A., Ahmed, H., Engström, F., Samuelsson, C., Characterization and evaluation of the pozzolanic activity of granulated copper slag modified with CaO (2019) J. Clean. Prod., 232, pp. 1112-1120; Gökçe, H.S., High temperature resistance of boron active belite cement mortars containing fly ash (2019) J. Clean. Prod., 211, pp. 992-1000; Hooton, R., Al-Jabri, K., Taha, R., Al-Harthy, A., Al-Oraimi, S., Al-Nuaimi, A., Use of cement by-pass dust in flowable fill mixtures (2002) Cem. Concr. Aggregates, 24, pp. 53-57; Huang, X.Q., Hou, H.B., Zhou, M., Wang, W.X., Mechanical properties and microstructure analysis of copper tailings solidifying with different cementitious materials (2014) Adv. Mater. Res., 878, pp. 171-176; Kovtun, M., Ziolkowski, M., Shekhovtsova, J., Kearsley, E., Direct electric curing of alkali-activated fly ash concretes: a tool for wider utilization of fly ashes (2016) J. Clean. Prod., 133, pp. 220-227; Krivenko, P., Petropavlovsky, O., Petranek, V., Pushkar, V., Vozniuk, G., High strength alkali activated slag cements with controlled setting times and early strength gain (2015) Adv. Mater. Res., 1100, pp. 44-49; Le, K., Ke, Z., Yao-Jun, Z., Li, Z., Meng-Yang, Y., Research progresses of new type Alkali-activated cementitious material catalyst (2016) J. Inorg. Mater., 31, p. 225; Lo, T.Y., Cui, H., Memon, S.A., Noguchi, T., Manufacturing of sintered lightweight aggregate using high-carbon fly ash and its effect on the mechanical properties and microstructure of concrete (2016) J. Clean. Prod., 112, pp. 753-762; Lothenbach, B., Scrivener, K., Hooton, R.D., Supplementary cementitious materials (2011) Cement Concr. Res., 41, pp. 1244-1256; Lu, H., Qi, C., Chen, Q., Gan, D., Xue, Z., Hu, Y., A new procedure for recycling waste tailings as cemented paste backfill to underground stopes and open pits (2018) J. Clean. Prod., 188, pp. 601-612; Mithun, B.M., Narasimhan, M.C., Performance of alkali activated slag concrete mixes incorporating copper slag as fine aggregate (2016) J. Clean. Prod., 10, pp. 7-18; Mo, L., Zhang, F., Panesar, D.K., Deng, M., Development of low-carbon cementitious materials via carbonating Portland cement–fly ash–magnesia blends under various curing scenarios: a comparative study (2017) J. Clean. Prod., 163, pp. 252-261; Moukannaa, S., Loutou, M., Benzaazoua, M., Vitola, L., Alami, J., Hakkou, R., Recycling of phosphate mine tailings for the production of geopolymers (2018) J. Clean. Prod., 185, pp. 891-903; Narattha, C., Chaipanich, A., Phase characterizations, physical properties and strength of environment-friendly cold-bonded fly ash lightweight aggregates (2018) J. Clean. Prod., 171, pp. 1094-1100; Onuaguluchi, O., Eren, Ö., Cement mixtures containing copper tailings as an additive: durability properties (2012) Mater. Res., 15, pp. 1029-1036; Onuaguluchi, O., Eren, Ö., Reusing copper tailings in concrete: corrosion performance and socioeconomic implications for the Lefke-Xeros area of Cyprus (2016) J. Clean. 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Soc., 6, pp. 16-25; U.S. Geological Survey, Mineral commodity summaries 2017: U.S. Geological Survey, mineral commodity summaries 2017 (2017); Vargas, F., Lopez, M., Development of a new supplementary cementitious material from the activation of copper tailings: mechanical performance and analysis of factors (2018) J. Clean. Prod., 182, pp. 427-436; Waltz, R.A., Morales, J.L., Nocedal, J., Orban, D., An interior algorithm for nonlinear optimization that combines line search and trust region steps (2006) Math. Program., 107, pp. 391-408; Weng, L., Sagoe-Crentsil, K., Dissolution processes, hydrolysis and condensation reactions during geopolymer synthesis: Part I-Low Si/Al ratio systems (2007) J. Mater. Sci., 42, pp. 2997-3006; Wongsa, A., Boonserm, K., Waisurasingha, C., Sata, V., Chindaprasirt, P., Use of municipal solid waste incinerator (MSWI) bottom ash in high calcium fly ash geopolymer matrix (2017) J. Clean. Prod., 148, pp. 49-59; Xu, B., Pu, X., Study on the relationship between the phase separation of slag glass and the latent hydraulic activity of bfs (1997) J. Chin. Ceram. Soc., 25, pp. 105-109; Yin, S., Wang, L., Wu, A., Free, M.L., Kabwe, E., Enhancement of copper recovery by acid leaching of high-mud copper oxides: a case study at Yangla Copper Mine, China (2018) J. Clean. Prod., 202, pp. 321-331; Yin, S., Wang, L., Wu, A., Kabwe, E., Chen, X., Yan, R., Copper recycle from sulfide tailings using combined leaching of ammonia solution and alkaline bacteria (2018) J. Clean. Prod., 189, pp. 746-753; Zhang, Y.J., Zhao, Y.L., Li, H.H., Xu, D.L., Structure characterization of hydration products generated by alkaline activation of granulated blast furnace slag (2008) J. Mater. Sci., 43, pp. 7141-7147; Zhang, C., Zhou, T., Wu, Q., Zhu, H., Xu, P., Mechanical performances and microstructures of cement containing copper tailings (2014) Asian J. Chem., 26, pp. 1371-1375; Zhu, P., Preliminary study on the relationship between structure and activity of blast furnace slag (1983) J. Chin. Ceram. Soc., 11, pp. 290-296; Zunino, F., Lopez, M., Decoupling the physical and chemical effects of supplementary cementitious materials on strength and permeability: a multi-level approach (2016) Cement Concr. Compos., 65, pp. 19-28 [format_title_en_publication_en_pub_year] => 3c7e48f10de2d7c3a513698a8b16feb0389194224 [abstract_en] => During copper ore smelting process, large amount of copper slag is created, which has excellent use in variance ways. In the common case, copper slag were deposited on the surface without treatment, which inevitably caused serious environmental problems. To deal with the pollution created and better utilize copper slag, a new controlled low strength filling material (CLSFM) was proposed for the copper slag from a mine in Africa.The filling cost of using CLSFM is equivalent to that of cement, as well as the method is more ecological and conforms to the local government environment policy. In order to study the influencing factors of this material, orthogonal test method and data visualization were used in the experiment, which make it possible to observe the effect of multifactor composite action on copper slag and the optimum ratio. The experimental results show that mechanical activation and alkali excitation can effectively activate the copper slag. Microscopic analysis shows that the main hydration product of CLSFM, C-S-H, makes the copper slag cementitious, and improves its structural strength. (C) 2019 Elsevier Ltd. All rights reserved. [scopus_id] => 57196459228;7402998420;57211942773; [from_id] => 76,74,73 [cauthor_ad] => [Wu, AX]Univ Sci & Technol Beijing, Sch Civil & Environm Engn, Beijing 100083, Peoples R China. [hx_id] => 2376,2378,2371 [classification_pub] => JCROE [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11,3 [title_en] => Development of a new controlled low strength filling material from the activation of copper slag: Influencing factors and mechanism analysis [index_keyword] => Activation analysis; Chemical activation; Data visualization; Filling; Hydration; Ores; Slags; Strength of materials; Surface treatment; Testing; Alkali activation; Copper slag; Gelling agents; Hydration mechanisms; Mechanical activation; Copper smelting [volume] => 246 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Copper slag; Mechanical activation; Alkali activation; Filling gelling; agent; Hydration mechanism [article_id] => 814903,809047,810314 [hints] => 1 [publisher] => ELSEVIER SCI LTD [doi] => 10.1016/j.jclepro.2019.119060 [language] => English [issn] => 0959-6526 [batch] => 3422,3418,3424 [publication_en] => JOURNAL OF CLEANER PRODUCTION [email] => wuaixiang@xs.ustb.edu.cn [document_No] => 119060 [format_title_en_issn_pub_year] => 1f9b3a3a221cd17d5f066ecca108bc09-594812682 [publication_iso] => J. Clean Prod. [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Lan, Wentao; Wu, Aixiang; Yu, Ping [ei_No] => 20194807742384 [main_eword] => Copper smelting [format_publication_cn] => [format_title_en] => e86a727dec9639bd03c7e6655f1aa14c-1213677467 [pub_date] => FEB 10 [classification_No] => 533.2 Metal Refining - 691.2 Materials Handling Methods - 723.2 Data Processing and Image Processing - 804 Chemical Products Generally - 951 Materials Science [cauthor_order] => 2,2 [uncontrolled_terms] => Alkali activation - Copper slag - Gelling agents - Hydration mechanisms - Mechanical activation [controlled_terms] => Activation analysis - Chemical activation - Data visualization - Filling - Hydration - Ores - Slags - Strength of materials - Surface treatment - Testing [reference_No] => 38 [format_ei_No] => aace64d08d892a53851016d6f950519d1540594132 [sys_priority_field] => 73 [cauthor_back] => Wu, Aixiang@@@Wu, AX [format_publication_en] => a2f6dd8ed4d0a09d098ba6e484d68d3a-1376919924 [cite_wos] => 0 [check_3Y] => 10 [delivery_No] => JY8BF [pages] => 10 [publication_29] => J CLEAN PROD [eissn] => 1879-1786 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => CEMENTITIOUS MATERIALS; PASTE BACKFILL; TAILINGS; ASH; HYDRATION; PERFORMANCE; MINE [fund_ab] => This study was financially supported by the National Key R&D Program of; China(Grant No.2017YFC0602903), the Key Program of National Natural; Science Foundation of China (Grant No. 51734001), the National Science; Fund for Excellent Young Scholars (Grant No. 51722401). [publisher_city] => OXFORD [cite_awos] => 0 [wos_No] => WOS:000504632600103 [format_wos_No] => 5fc2ccc46866985b0980e1eafc1a2870115816249 [wos_sub] => Green & Sustainable Science & Technology; Engineering, Environmental;; Environmental Sciences [research_area] => Science & Technology - Other Topics; Engineering; Environmental Sciences; & Ecology [check_180] => 10 [publisher_ad] => THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => journalofcleanerproduction [jl_country] => 中国 [jl_keyword_en] => hydrationmechanism,fillinggelling,agent,alkaliactivation,mechanicalactivation,copperslag [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elseviersciltd [company_id] => 0,0 [author_id] => [id] => ZgA103ABe-eYmRwwJhsm [tags] => 0 ) [12] => Array ( [standard_in] => School of Material Science and Engineering, University of Jinan, Jinan, 250022, China; College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu 210037, China; School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Key Laboratory of Low Carbon Energy and Chemical Engineering, College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China [cauthor] => Yu, Zhenjie(yuzhenjie19940929@163.com) [school_id] => 117 [scopus_No] => 2-s2.0-85076488233 [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076488233&doi=10.1016%2fj.ijhydene.2019.11.145&partnerID=40&md5=97a1a8d6b6461dcaf30da5246b45c63a [tag] => 0 [author_en] => Yu, ZJ; Zhang, LJ; Zhang, ZM; Zhang, S; Hu, S; Xiang, J; Wang, Y; Liu, Q; Liu, QH; Hu, X [format_scopus_No] => 3e887681c56061af17021078dee11c032004373960 [format_doi] => 61c3b4fe4f6653abaf79dd7b622a48121641982384 [author_in] => [Yu, Zhenjie; Zhang, Lijun; Zhang, Zhanming; Liu, Qianhe; Hu, Xun] Univ Jinan, Sch Mat Sci & Engn, Jinan 250022, Peoples R China.@@@ [Zhang, Shu] Nanjing Forestry Univ, Coll Mat Sci & Engn, Nanjing 210037, Jiangsu, Peoples R China.@@@ [Hu, Song; Xiang, Jun; Wang, Yi] Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan 430074, Peoples R China.@@@ [Liu, Qing] Shandong Univ Sci & Technol, Coll Chem & Environm Engn, Key Lab Low Carbon Energy & Chem Engn, Qingdao 266590, Shandong, Peoples R China. [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [51876080]; Strategic International Scientific and; Technological Innovation Cooperation Special Funds of National Key; Research and Development Program of China [2016YFE0204000]; Program for; Taishan Scholars of Shandong Province Government; Recruitment Program of; Global Experts (Thousand Youth Talents Plan); Natural Science Foundation; of Shandong ProvinceNatural Science Foundation of Shandong Province; [ZR2017BB002]; Key Research and Development Program of Shandong Province; [2018GSF116014] [reference] => Chen, C., Budi, C., Wu, H., Saikia, D., Kao, H., Size-Tunable Ni nanoparticles supported on surface-modifled, cage type mesoporous silica as highly active catalysts for CO2 hydrogenation (2017) Appl Catal B-Environ, 12, pp. 8367-8381; Munnik, P., Wolters, M., Gabrielsson, A., Pollington, S.D., Headdock, G., Bitter, J.H., Jongh, P.E., Jong, K.P., Copper nitrate redispersion to arrive at highly active silica-supported copper catalysts (2011) J Phys Chem C, 115, pp. 14698-14706; Li, Z., Das, S., Hongmanorom, P., Dewangan, N., Wai, M., Kawai, S., Silica-based micro and mesoporous catalysts for dry reforming of methane (2018) Catal. 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Rev., 93, pp. 196-202; Askari, S., Haq, A.U., Macias-Montero, M., Levchenko, I., Yu, F., Zhou, W., Ostrikov, K., Mariotti, D., Ultra-small photoluminescent silicon-carbide nanocrystals by atmospheric-pressure plasmas (2016) Nanoscale, 8, pp. 17141-17149; Kucinski, K., Hreczycho, G., Catalytic formation of silicon–heteroatom (N, P, O, S) bonds (2017) ChemCatChem, 9, pp. 1868-1885; Zhang, T., Lu, Y., Li, W., Su, M., Yang, T., Ogunbiyi, A., Jin, Y., One-pot production of γ-valerolactone from furfural using Zr-graphitic carbon nitride/H-β composite (2019) Int J Hydrogen Energy, 44, pp. 14527-14535; Krishnan, P.S., Tamizhdurai, P., Alagarasi, A., Shanthi, K., Vapour phase hydrodeoxygenation of furfural into fuel grade compounds on NiPMoS catalyst: synergetic effect of NiP and laponite support (2019) Int J Hydrogen Energy, 44, pp. 14968-14980; Shao, Y., Sun, K., Zhang, L., Xu, Q., Zhang, Z., Li, Q., Zhang, S., Hu, X., Balanced distribution of Brønsted acid sites and Lewis acid sites for highly selective conversion of xylose into levulinic acid/ester over Zr-beta catalysts (2019) Green Chem; Asthana, S., Samanta, C., Bhaumik, A., Banerjee, B., Voolapalli, R.K., Saha, B., Direct synthesis of dimethyl ether from syngas over Cu-based catalysts: enhanced selectivity in the presence of MgO (2016) J Catal, 334, pp. 89-101; Karaman, B.P., Cakriyilmaz, N., Arbag, H., Oktar, N., Dogu, G., Dogu, T., Performance comparison of mesoporous alumina supported Cu & Ni based catalysts in acetic acid reforming (2017) Int J Hydrogen Energy, 42, pp. 26257-26269; Liu, L., Lou, H., Chen, M., Selective hydrogenation of furfural to tetrahydrofurfuryl alcohol over Ni/CNTs and bimetallic CuNi/CNTs catalysts (2016) Int J Hydrogen Energy, 41, pp. 14721-14731; Yfanti, V.L., Lpsakis, D., Lemonido, A.A., Kinetic study of liquid phase glycerol hydrodeoxygenation under inert conditions over a Cu-based catalyst (2014) React. Chem. Eng., 4, pp. 212-226; Jimenez-Gomez, C.P., Cecilia, J.A., Duran-Martin, D., Mroeno-Tost, R., Santamaria-Gonzalez, J., Merida-Robles, J., Mariscal, R., Maireles-Torres, P., Gas-phase hydrogenation of furfural to furfuryl alcohol over Cu/ZnO catalysts (2016) J Catal, 223, pp. 107-115; Zhang, L., Hu, G., Hu, S., Xiang, J., Hu, X., Wang, Y., Geng, D., Hydrogenation of fourteen biomass-derived phenolics in water and in methanol: their distinct reaction behavior (2018) Sustainable Energy Fuels, 2, pp. 751-758 [format_title_en_publication_en_pub_year] => 4fef2307ffc87f8292cbe6ed5eaafae5-22842757 [abstract_en] => The silica-based materials with varied pore distribution were prepared by adjusting the pH during the self-assembly process and were used as the supporting materials of Cu-based catalysts for hydrogenation of furfural and phenolics. It was found that the pH during the self-assembly process drastically impacted the porous structures of the resulting silica materials. At a lower pH environment (pH = 1.0), the accelerated hydrolysis rate of Na2SiO3 led to the randomly arranged Si species, forming the disordered structures with the pore size of ca. 7 nm. At the higher pH environment (pH = 2.0), the slow hydrolysis rate created the difficulty for grow of silicon precursor on the template, and the disordered mesoporous structures of silica with the pore size of ca. 12 nm were formed. The moderate hydrolysis rate at pH of 1.5 favoured the formation of the ordered pores with the pore size of ca. 3 nm. The results indicated the pores of different sizes induced the distinct steric hindrance, which further affected the efficiency for hydrogenation of the organics with ring structures. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. [scopus_id] => 57202456992;55042119100;57194275091;57202122961;57208674601;7201546185;54929772200;57192837043;43861612400;55347561200; [from_id] => 76,74,73 [cauthor_ad] => [Yu, ZJ; Liu, QH]Univ Jinan, Sch Mat Sci & Engn, Jinan 250022, Peoples R China. [hx_id] => 2376,2378,2371 [classification_pub] => IJHED [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [format_issn_issue_page_pub_year] => d65d4ed077b640bbe04945796a10c1a6579995509 [title_en] => Silica of varied pore sizes as supports of copper catalysts for hydrogenation of furfural and phenolics: Impacts of steric hindrance [index_keyword] => Aldehydes; Assembly; Catalysts; Copper; Furfural; Hydrogenation; Hydrolysis; Silica; Sodium compounds; Cu-based catalyst; Disordered structures; Mesoporous structures; Self assembly process; Silica based materials; Silicon precursors; Steric hindrances; Supporting material; Pore size [volume] => 45 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Cu based catalysts; Silica-based materials; Furfural hydrogenation;; Steric hindrance [article_id] => 808335,813484,819708 [begin_page] => 2720 [hints] => 1 [publisher] => PERGAMON-ELSEVIER SCIENCE LTD [doi] => 10.1016/j.ijhydene.2019.11.145 [language] => English [issue] => 4 [issn] => 0360-3199 [batch] => 3422,3418,3424 [publication_en] => INTERNATIONAL JOURNAL OF HYDROGEN ENERGY [email] => yuzhenjie19940929@163.com; mse_liuqh@ujn.edu.cn [sys_update_time] => 2020-03-13 09:56:09 [format_title_en_issn_pub_year] => 88d29c1da3ab8aeede957d9000324ccd1628248990 [publication_iso] => Int. J. Hydrog. Energy [SYS_TAG] => 3 [end_page] => 2728 [page] => 2720-2728 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Yu, Zhenjie; Zhang, Lijun; Zhang, Zhanming; Zhang, Shu; Hu, Song; Xiang, Jun; Wang, Yi; Liu, Qing; Liu, Qianhe; Hu, Xun [ei_No] => 20195107870184 [numerical_index] => Size 1.20e-08m, Size 3.00e-09m, Size 7.00e-09m [main_eword] => Pore size [format_publication_cn] => [format_title_en] => d314ce180865b6e1205d3cf437ffa5a6-1793002704 [pub_date] => JAN 24 [classification_No] => 544.1 Copper - 802.2 Chemical Reactions - 803 Chemical Agents and Basic Industrial Chemicals - 804 Chemical Products Generally - 804.1 Organic Compounds - 951 Materials Science [cauthor_order] => 1,1,9 [uncontrolled_terms] => Cu-based catalyst - Disordered structures - Mesoporous structures - Self assembly process - Silica based materials - Silicon precursors - Steric hindrances - Supporting material [controlled_terms] => Aldehydes - Assembly - Catalysts - Copper - Furfural - Hydrogenation - Hydrolysis - Silica - Sodium compounds [reference_No] => 46 [format_ei_No] => 6647f46a40d72d5f350b74cae5a2a003130650193 [sys_priority_field] => 73 [cauthor_back] => Yu, Zhenjie@@@Yu, ZJ@@@Liu, QH [format_publication_en] => 098bccde16f9d144b529c7a6940ac778-1274215917 [cite_wos] => 0 [check_3Y] => 0 [delivery_No] => KL2YQ [pages] => 9 [publication_29] => INT J HYDROGEN ENERG [eissn] => 1879-3487 [orcID] => Yu, Zhenjie/0000-0002-6850-3363 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => MESOPOROUS SILICA; HYDRODEOXYGENATION; CU; PYROLYSIS; ALUMINA [fund_ab] => This work was supported by the National Natural Science Foundation of; China (No. 51876080), the Strategic International Scientific and; Technological Innovation Cooperation Special Funds of National Key; Research and Development Program of China (No. 2016YFE0204000), the; Program for Taishan Scholars of Shandong Province Government, the; Recruitment Program of Global Experts (Thousand Youth Talents Plan), the; Natural Science Foundation of Shandong Province (ZR2017BB002) and the; Key Research and Development Program of Shandong Province; (2018GSF116014). [publisher_city] => OXFORD [cite_awos] => 0 [wos_No] => WOS:000513294900023 [format_wos_No] => 7714cd404fd661fa35a6d8d0e82a3ac41223373086 [wos_sub] => Chemistry, Physical; Electrochemistry; Energy & Fuels [research_area] => Chemistry; Electrochemistry; Energy & Fuels [check_180] => 0 [publisher_ad] => THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => internationaljournalofhydrogenenergy [jl_country] => 中国 [jl_keyword_en] => silicabasedmaterials,furfuralhydrogenation,cubasedcatalysts,sterichindrance, [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => pergamonelsevierscienceltd [company_id] => 0,0 [author_id] => [id] => YwA003ABe-eYmRww6g7I [tags] => 0 ) [13] => Array ( [cauthor] => Xie, Weining(aacc_xwn5718@cumt.edu.cn) [issn] => 0016-2361 [school_id] => 117 [controlled_terms] => Coal breakage - Coal deposits - Coking - Coking properties - Energy efficiency - Genetic algorithms - Grinding (machining) - Mixtures - Size determination [batch2] => 15 [hb_batch] => 3418 [ei_No] => 20195007829689 [tag] => 0 [author_en] => Lu, QC; Xie, WN; Zhang, FB; He, YQ; Duan, CL; Wang, S; Zhu, XN [abstract_en] => Aiming to maximize the utilization of coal resource, heterogeneous grinding of coal in various coalification degrees is common. Interaction among particles of different coals in mixture breakage results in changes of energy-size reduction characteristics if compared with that of single grinding. In this paper, anthracite and coking coals of low ash are ground together in a Hardgrove mill, and resistance to be broken is compared based on the classical breakage model. Product t(10) of component after mixture breakage are determined by the relation between characteristic ratio (ratio of height and full width at half maximum of 002 peak) and yield of anthracite coal in mixture. Results indicate that product t(10) of anthracite coal increases as more coking coal is added, and that of coking coal shows the contrary trend. A new method is proposed for the determination of energy split factors of components based on the assumption that the relation t(10) and spilt energy of component still can be modelled by the classical breakage model. Besides energy split factors of components, breakage indictors are also determined according to above assumption and energy balance by genetic algorithm. Soft coking coal promote the energy-size reduction of hard anthracite coal in mixture breakage, with the increase of breakage indicator (A and b) and energy efficiency compared with those of single breakage. Energy split factors of anthracite coal is above one, and increases with the product fineness. And coking coal shows the contrary trend. [format_doi] => 13cd587cb40263ffb603e54ccdf428361100965919 [sys_update_time] => 2020-03-13 09:40:16 [cauthor_back] => Xie, Weining@@@Xie, WN [format_title_en_publication_en_pub_year] => a956299d739d5be3f3d1c5d8714010362092148942 [classification_No] => 503 Mines and Mining, Coal - 524 Solid Fuels - 525.2 Energy Conservation - 604.2 Machining Operations [document_No] => 116829 [main_eword] => Anthracite [from_id] => 76,74,73 [email] => aacc_xwn5718@cumt.edu.cn [classification_pub] => FUELA [datebase] => Scopus [sys_level_num] => 15_8 [sys_jg_type] => 11,3 [title_en] => Energy-size reduction of mixtures of anthracite and coking coal in Hardgrove mill [volume] => 264 [author_fn] => Lu, Qichang; Xie, Weining; Zhang, Fengbin; He, Yaqun; Duan, Chenlong; Wang, Shuai; Zhu, Xiangnan [pub_year] => 2020 [article_id] => 809621,810612,815032 [hints] => 1 [pub_date] => MAR 15 [publisher] => ELSEVIER SCI LTD [doi] => 10.1016/j.fuel.2019.116829 [language] => English [source_type] => 351 [reference_No] => 21 [batch] => 3422,3418,3424 [publication_en] => FUEL [hx_id] => 2376,2378,2371 [author_in] => [Lu, Qichang; Zhang, Fengbin; He, Yaqun; Duan, Chenlong] China Univ Min & Technol, Sch Chem Engn & Technol, Xuzhou 221116, Jiangsu, Peoples R China.@@@ [Xie, Weining; He, Yaqun; Wang, Shuai] China Univ Min & Technol, Adv Anal & Computat Ctr, Xuzhou 221116, Jiangsu, Peoples R China.@@@ [Xie, Weining] Jiangsu Huahong Technol Stock Ltd Co, Wuxi 214400, Jiangsu, Peoples R China.@@@ [Zhu, Xiangnan] Shandong Univ Sci & Technol, Sch Chem & Environm Engn, Qingdao 266590, Shandong, Peoples R China. [format_title_en_issn_pub_year] => fe46147e34852526167dcea20b5d98d9-138023590 [format_ei_No] => 82dccff616ffe5850245a4340d332d72-559665456 [cauthor_order] => 2,2 [uncontrolled_terms] => Breakage models - Characteristic ratio - Coal resources - Different coals - Energy split - Size reductions - Specific energy - Various coalification degrees [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [fund_No] => Fundamental Research Funds for the Central UniversitiesFundamental; Research Funds for the Central Universities [2017XKQY054]; Priority; Academic Program Development of Jiangsu Higher Education Institutions [index_keyword] => Coal breakage; Coal deposits; Coking; Coking properties; Energy efficiency; Genetic algorithms; Grinding (machining); Mixtures; Size determination; Breakage models; Characteristic ratio; Coal resources; Different coals; Energy split; Size reductions; Specific energy; Various coalification degrees; Anthracite [format_title] => [reference] => Liu, M., Zhang, X.W., Yang, K.X., Ma, Y.G., Yan, J.J., Optimization and comparison on supercritical CO2 power cycles integrated within coal-fired power plants considering the hot and cold end characteristics (2019) Energ Conver Manage, 195 (1), pp. 854-865; Mohammad, R., Venkat, N.R., Rick, Q.H., Conceptual modifications of coal preparation plants to minimize potential environmental issues associated with coal waste disposal (2019) Fuel, 250, pp. 352-361; Zhao, R.C., Han, Y.X., He, M.Z., Li, Y.J., Grinding kinetics of quartz and chlorite in wet ball milling (2017) Powder Technol, 305, pp. 418-425; Ipek, H., Ucbaş, Y., Yekeler, M., Hoşten, Ç., Dry grinding kinetics of binary mixtures of ceramic raw materials by Bond milling (2005) Ceramics Int, 31 (8), pp. 1065-1071; Deniz, V., Comparisons of dry grinding kinetics of lignite, bituminous coal and petroleum coke (2013) Energy Sources Part A, 35 (10), pp. 913-920; Heechan, C., Luckie, P., Investigation of the breakage properties of components in mixtures ground in a batch ball-and-race mill (1995) Energ Fuel, 9 (1), pp. 53-58; Yu, J.D., He, Y.Q., Hao, J., Liu, F.Y.Z., Li, H., Wang, C., A mathematical model to characterize the degree of coalification based on the low-angle region of the X-ray (2018) J X-ray Sci Technol, 39 (1), pp. 1-11; Zhang, W., Chen, F., Wu, D., An experimental study on the evolution of aggregate structure in coals of different ranks by in situ X-ray diffractometry (2015) Anal Methods, 7, pp. 8720-8726; Umucu, Y., Deniz, V., Çayirli, S., A new model for comminution behavior of different coals in an impact crusher (2014) Energy Sources Part A, 36 (13), pp. 1406-1413; Dündar, H., Hakan, B., Investigating multicomponent breakage in cement grinding (2015) Miner Eng, 77, pp. 131-136; Phatak, P.B., Effect of particulate environment on the kinetics and energetics of dry ball milling (2010) Int J Miner Process, 97, pp. 52-58; Abouzeid, A.Z.M., Fuerstenau, D.W., Grinding of mineral mixtures in high-pressure grinding rolls (2009) Int J Miner Process, 93, pp. 59-65; Sun, X., Xie, W., He, Y., Li, H., Wang, S., Zhu, X., Analyses of the energy-size reduction of mixtures of narrowly sized coals in a ball-and-race mill (2018) Adv Powder Technol, 184, pp. 1357-1365; Xie, W., He, Y., Yang, Y., Shi, F., Huang, Y., Li, H., Experimental investigation of breakage and energy consumption characteristics of mixtures of different components in vertical spindle pulverizer (2017) Fuel, 190, pp. 208-220; Xie, W., He, Y., Ge, Z., Shi, F., Yang, Y., Li, H., An analysis of the energy split for grinding coal/calcite mixture in a ball-and-race mill (2016) Miner Eng, 93, pp. 1-9; Deniz, V., Umucu, Y., Interrelationships between the bond grindability with physicomechanical and chemical properties of coals (2013) Energy Sources Part A, 35 (2), pp. 144-151; Xie, W., He, Y., Zhang, Y., Huang, Y., Li, H., Wei, H., Simulation study of the energy-size reduction of MPS vertical spindle pulverizer (2015) Fuel, 139, pp. 180-189; Deniz, V., A new size distribution model by t-family curves for comminution of limestones in impact crusher (2011) Adv Powder Technol, 22 (6), pp. 761-765; Shi, F., Coal breakage characterisation – Part 2: Multi-component breakage modelling (2014) Fuel, 117, pp. 1156-1162; Shi, F., A review of the applications of the JK size-dependent breakage model part 3: Comminution equipment modelling (2016) Int J Miner Process, 157, pp. 60-72; Deniz, V., Evaluation of grindability behaviors of four different solid fuels blending by using the Hardgrove Mill (2019) Inzynieria Mineralna – J Polish Mineral Eng, 43 (1), pp. 35-42 [scopus_id] => 57205626067;46761586200;57212220718;8853879300;8853879100;57207088275;55837874100; [format_scopus_No] => 7600d37e2d2f1702a00ea7ee172e6a7d1607293447 [keyword_en] => Heterogeneous grinding; Various coalification degrees; Energy-size; reduction; Split specific energy; Genetic algorithm [format_publication_cn] => [publication_iso] => Fuel [format_title_en] => 38e51ba73905af81e7ee2b36a77e3c60970812108 [sys_priority_field] => 73 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076245901&doi=10.1016%2fj.fuel.2019.116829&partnerID=40&md5=7d0c797fe52955b42d1913f5e230fb09 [standard_in] => School of Chemical Engineering and Technology, China University of Mining & Technology, Xuzhou, Jiangsu 221116, China; Advanced Analysis & Computation Center, China University of Mining & Technology, Xuzhou, Jiangsu 221116, China; Jiangsu Huahong Technology Stock Limited Company, Wuxi 214400, Jiangsu, China; School of Chemical & Environmental Engineering, Shandong University of Science & Technology, Qingdao, Shandong 266590, China [scopus_No] => 2-s2.0-85076245901 [format_publication_en] => 7c4e4db5ec9a751ee4bbfba997f0a3a7834498537 [cite_wos] => 0 [check_3Y] => 6 [delivery_No] => KA2ZV [cauthor_ad] => [Xie, WN]China Univ Min & Technol, Adv Anal & Computat Ctr, Xuzhou 221116, Jiangsu, Peoples R China. [pages] => 7 [publication_29] => FUEL [eissn] => 1873-7153 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => DRY GRINDING KINETICS; MULTICOMPONENT BREAKAGE [fund_ab] => Works in this paper is supported by the Fundamental Research Funds for; the Central Universities (2017XKQY054), and the Priority Academic; Program Development of Jiangsu Higher Education Institutions. Authors; also thank for the technical support of Advanced Analysis & Computation; Center. [publisher_city] => OXFORD [cite_awos] => 0 [wos_No] => WOS:000505667000074 [format_wos_No] => 8d833d503efd8c9e57058dd9abcaddad-180095094 [wos_sub] => Energy & Fuels; Engineering, Chemical [research_area] => Energy & Fuels; Engineering [check_180] => 6 [publisher_ad] => THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => fuel [jl_country] => 中国 [jl_keyword_en] => variouscoalificationdegrees,geneticalgorithm,splitspecificenergy,reduction,energysize,heterogeneousgrinding [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elseviersciltd [company_id] => 0,0 [author_id] => [id] => EwA303ABe-eYmRww8iK5 [tags] => 0 ) [14] => Array ( [cauthor] => Fan, Jian-cong(fanjiancong@sdust.edu.cn) [issn] => 1868-8071 [school_id] => 117 [controlled_terms] => Copying - Fuzzy clustering - Fuzzy systems [batch2] => 15 [hb_batch] => 3418 [ei_No] => 20193207277679 [tag] => 0 [author_en] => Liu, XY; Fan, JC; Chen, ZW [abstract_en] => Fuzzy C-means (FCM) algorithm is a fuzzy clustering algorithm based on objective function compared with typical \"hard clustering\" such as k-means algorithm. FCM algorithm calculates the membership degree of each sample to all classes and obtain more reliable and accurate classification results. However, in the process of clustering, FCM algorithm needs to determine the number of clusters manually, and is sensitive to the initial clustering center. It is easy to generate problems such as multiple clustering iterations, slow convergence speed and local optimal solution. To address those problems, we propose to combine the FCM algorithm and DPC (Clustering by fast search and find of density peaks) algorithm. First, DPC algorithm is used to automatically select the center and number of clusters, and then FCM algorithm is used to realize clustering. The comparison experiments show that the improved FCM algorithm has a faster convergence speed and higher accuracy. [format_doi] => 7fdac4e2097fa304a2897cbcf27ff7da-1563753192 [author_in] => [Liu, Xiang-yi; Fan, Jian-cong; Chen, Zi-wen] Shandong Univ Sci, Coll Comp Sci, Engn, Technology, Qingdao, Peoples R China.@@@ [Fan, Jian-cong] Shandong Univ Sci, Prov Key Lab Informat Technol Wisdom Min Shandong, Technology, Qingdao, Peoples R China.@@@ [Fan, Jian-cong] Shandong Univ Sci, Prov Expt Teaching Demonstrat Ctr Comp, Technology, Qingdao, Peoples R China. [cauthor_back] => Fan, Jiancong@@@Fan, JC@@@Fan, JC@@@Fan, JC [format_title_en_publication_en_pub_year] => 675171c28166a5af7a7e04623197400f855438299 [classification_No] => 723 Computer Software, Data Handling and Applications - 745.2 Reproduction, Copying - 961 Systems Science [main_eword] => K-means clustering [format_ei_No] => e90d8eea035bee218ca8b0f6ac6d717c-210718213 [from_id] => 74,73 [issue] => 3 [email] => fanjiancong@sdust.edu.cn [cauthor_order] => 2,2,2,2 [datebase] => Compendex;Compilation and indexing terms, Copyright 2020 Elsevier Inc. [sys_level_num] => 15_8 [sys_jg_type] => 11,9 [format_issn_issue_page_pub_year] => f0622a9ef541c2709a3cca76fcfc1f631550114332 [title_en] => Improved fuzzy C-means algorithm based on density peak [volume] => 11 [author_fn] => Liu, Xiang-yi; Fan, Jian-cong; Chen, Zi-wen [pub_year] => 2020 [eissn] => 1868-808X [pub_date] => MAR [begin_page] => 545 [hints] => 1 [publisher] => SPRINGER HEIDELBERG [doi] => 10.1007/s13042-019-00993-8 [language] => English [source_type] => 351 [reference_No] => 40 [batch] => 3422,3418 [publication_en] => INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS [hx_id] => 2376,2371 [sys_update_time] => 2020-03-13 09:40:18 [format_title_en_issn_pub_year] => ae6e8063796441737748f3a4bfc3e62d2136756233 [article_id] => 810649,807569 [uncontrolled_terms] => Classification results - Clustering - Fuzzy C-means algorithms - Improved fcm algorithms - Improved fuzzy c-means - Initial clustering centers - Local optimal solution - Multiple clusterings [SYS_TAG] => 3 [end_page] => 552 [page] => 545-552 [hb_type] => 2 [article_dt] => Article [cite_wos] => 0 [check_3Y] => 0 [delivery_No] => KL2UG [format_title] => [cauthor_ad] => [Fan, JC]Shandong Univ Sci, Coll Comp Sci, Engn, Technology, Qingdao, Peoples R China@@@[Fan, JC]Shandong Univ Sci, Prov Key Lab Informat Technol Wisdom Min Shandong, Technology, Qingdao, Peoples R China@@@[Fan, JC]Shandong Univ Sci, Prov Expt Teaching Demonstrat Ctr Comp, Technology, Qingdao, Peoples R China. [pages] => 8 [publication_29] => INT J MACH LEARN CYB [publication_type] => J [get_data] => 2020-03-06 [keyword_en] => Fuzzy C-means algorithm; Density peak; Clustering [format_publication_cn] => [keyword_plu] => CLUSTERING-ALGORITHM; FAST SEARCH; COMPLEXITY; FIND [publication_iso] => Int. J. Mach. Learn. Cybern. [format_title_en] => 67e30247c7b984fbeadeffdb7704f86d2103447659 [publisher_city] => HEIDELBERG [cite_awos] => 0 [wos_No] => WOS:000513283300004 [sys_priority_field] => 73 [wos_sub] => Computer Science, Artificial Intelligence [format_wos_No] => 144016aa2c7670bcb17ef0699a8bdd5f1278204745 [research_area] => Computer Science [check_180] => 0 [publisher_ad] => TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY [format_publication_en] => 3f2adcbbe28c751e878926c24e396434285186803 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => internationaljournalofmachinelearningandcybernetics [jl_country] => 中国 [jl_keyword_en] => clustering,fuzzycmeansalgorithm,densitypeak [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => springerheidelberg [company_id] => 0,0 [author_id] => [id] => RQA403ABe-eYmRwwAiSz [tags] => 0 ) [15] => Array ( [cauthor] => Wang, Chengshan(chshwang@cugb.edu.cn) [issn] => 0921-8181 [school_id] => 117 [controlled_terms] => Carbon - Charcoal - Climate change - Coal - Coal combustion - Coal deposits - Polycyclic aromatic hydrocarbons - Spectrum analysis - Wetlands [batch2] => 15 [hb_batch] => 3418 [ei_No] => 20194407604571 [tag] => 0 [author_en] => Zhang, ZH; Wang, CS; Lv, DW; Hay, WW; Wang, TT; Cao, S [abstract_en] => As an important terrestrial carbon reservoir, peatland has the potential to influence the global carbon cycle and global climate. In recent decades, the frequency and extent of peatland wildfires in boreal and tropical regions are increasing owing to climate change and human activity. However, the processes that govern changes in peatland wildfire are poorly understood over long timescales, particularly on the orbital scales. We analyzed coal petrology and geochemistry in coal seams of the Aalenian Yan\'an Formation in Ordos Basin to identify peatland wildfire records based on the co-occurrence of charcoal and pyrolytic polycyclic aromatic hydrocarbons (PAHs). In addition, the presence of widespread peatland wildfires has suggested that atmospheric oxygen concentration at the time should be substantially higher than the minimum needed for sustained combustion. Spectral analysis of vitrinite/inertinite (V/I) and inertinite reflectance data demonstrate that peatland wildfires were controlled by precession cycles owing to a shift in the seasonal contrast and amount of rainfall. Our results provide essential context for understanding the importance of peatland wildfires carbon emissions in a coupling of orbital forcing, climate, and the carbon cycle. [format_doi] => 1ea0f5dae73d4f96150795e5de36d1df622004152 [sys_update_time] => 2020-03-13 09:40:17 [cauthor_back] => Wang, Chengshan@@@Wang, CS@@@Lv, DW [format_title_en_publication_en_pub_year] => cf57639320e75019b03ff20a4dc26ec0862257241 [classification_No] => 443.1 Atmospheric Properties - 503 Mines and Mining, Coal - 524 Solid Fuels - 804 Chemical Products Generally - 804.1 Organic Compounds - 914.2 Fires and Fire Protection [document_No] => 103051 [main_eword] => Fires [from_id] => 76,74,73 [email] => chshwang@cugb.edu.cn; lvdawei95@163.com [classification_pub] => GPCHE [datebase] => Scopus [sys_level_num] => 15_8 [sys_jg_type] => 11,3 [title_en] => Precession-scale climate forcing of peatland wildfires during the early middle Jurassic greenhouse period [volume] => 184 [author_fn] => Zhang, Zhihui; Wang, Chengshan; Lv, Dawei; Hay, William W.; Wang, Tiantian; Cao, Shuo [pub_year] => 2020 [article_id] => 812938,809089,818954 [hints] => 1 [pub_date] => JAN [publisher] => ELSEVIER [doi] => 10.1016/j.gloplacha.2019.103051 [language] => English [source_type] => 351 [reference_No] => 114 [batch] => 3422,3418,3424 [publication_en] => GLOBAL AND PLANETARY CHANGE [hx_id] => 2376,2378,2371 [author_in] => [Zhang, Zhihui; Wang, Chengshan; Wang, Tiantian; Cao, Shuo] China Univ Geosci, State Key Lab Biogeol & Environm Geol, Beijing 100083, Peoples R China.@@@ [Zhang, Zhihui; Wang, Chengshan; Lv, Dawei; Cao, Shuo] China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.@@@ [Lv, Dawei] Shandong Univ Sci & Technol, Shandong Prov Key Lab Deposit Mineralizat & Sedim, Qingdao 266590, Shandong, Peoples R China.@@@ [Hay, William W.] Univ Colorado Boulder, Dept Geol Sci, 2045 Windcliff Dr, Estes Pk, CO 80517 USA.@@@ [Wang, Tiantian] China Univ Geosci, Inst Earth Sci, Beijing 100083, Peoples R China. [format_title_en_issn_pub_year] => 1fccef0c835d1579c3c1872a7d4f6525-1883412763 [format_ei_No] => 7b97baaa1fbf2cc1bb3d2c535ad5bc221429197963 [cauthor_order] => 2,2,3 [uncontrolled_terms] => Atmospheric oxygen - Carbon reservoirs - Global carbon cycle - Jurassic - Orbital forcing - Polycyclic aromatic hydrocarbons (PAHS) - Sustained combustion - Wildfires [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [41790450, 41772096]; National Key RD Plan; [2017YFC0601405] [index_keyword] => Carbon; Charcoal; Climate change; Coal; Coal combustion; Coal deposits; Polycyclic aromatic hydrocarbons; Spectrum analysis; Wetlands; Atmospheric oxygen; Carbon reservoirs; Global carbon cycle; Jurassic; Orbital forcing; Polycyclic aromatic hydrocarbons (PAHS); Sustained combustion; Wildfires; Fires; Aalenian; carbon cycle; charcoal; climate change; climate forcing; coal; coal seam; global climate; human activity; Jurassic; orbital forcing; PAH; peatland; wildfire; China; Ordos Basin [format_title] => [reference] => Algeo, T.J., Ingall, E., Sedimentary Corg:P ratios, paleocean ventilation, and Phanerozoic atmospheric pO2 (2007) Palaeogeogr. 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Change [format_title_en] => 9efaf6832820f5f33587304124b56c211337741482 [sys_priority_field] => 73 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074101095&doi=10.1016%2fj.gloplacha.2019.103051&partnerID=40&md5=b0110bf6a38f6322a47059254f44f0cf [standard_in] => State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing, 100083, China; School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China; Shandong Provincial Key Laboratory of Depositional Mineralization and Sedimentary Minerals, Shandong University of Science and Technology, Qingdao, Shandong 266590, China; Department of Geological Sciences and University Museum, University of Colorado at Boulder, 2045 Windcliff Dr, Estes Park, CO 80517, United States; Institute of Earth Sciences, China University of Geosciences, Beijing, 100083, China [scopus_No] => 2-s2.0-85074101095 [format_publication_en] => 3bca9ddf3ae29348049da8c0772fde3065679664 [cite_wos] => 0 [check_3Y] => 2 [delivery_No] => KE3XD [cauthor_ad] => [Wang, CS; Lv, DW]China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China. [pages] => 13 [publication_29] => GLOBAL PLANET CHANGE [open_type] => Bronze [eissn] => 1872-6364 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => POLYCYCLIC AROMATIC-HYDROCARBONS; NET PRIMARY PRODUCTIVITY; ATMOSPHERIC OXYGEN; ORDOS BASIN; CARBON ACCUMULATION; FOSSIL CHARCOAL; FOREST-FIRES; COAL; HISTORY; RECORD [fund_ab] => We thank Dongzhao An for help with field works, Changyong Lu and Yi Yang; for assistance with coal petrology analysis. This study was financially; supported by the National Natural Science Foundation of China (grant No.; 41888101), the National Key R&D Plan (grant No. 2017YFC0601405) and the; National Natural Science Foundation of China (grants 41790450,; 41772096). [publisher_city] => AMSTERDAM [cite_awos] => 0 [wos_No] => WOS:000508491500008 [format_wos_No] => 95be60c6b8593cb8eb15e521f6967032-2105460619 [wos_sub] => Geography, Physical; Geosciences, Multidisciplinary [research_area] => Physical Geography; Geology [check_180] => 2 [publisher_ad] => RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => globalandplanetarychange [jl_country] => 中国 [jl_keyword_en] => coal,wildfires,orbitalforcing,jurassic,charcoal [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elsevier [company_id] => 0,0 [author_id] => [id] => MwA403ABe-eYmRwwBisB [tags] => 0 ) [16] => Array ( [standard_in] => Civil and Resource Engineering School, University of Science and Technology Beijing, Beijing, 100083, China; Center for Advanced Jet Engineering Technologies (CaJET), Key Laboratory of High-efficiency and Clean Mechanical Manufacture (Ministry of Education), School of Mechanical Engineering, Shandong University, Jinan, 250061, China; School of Chemical and Environmental Engineering, China University of Mining and Technology, Beijing, China [cauthor] => Huang, Jun(jun.huang@email.sdu.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85073071608 [author_in] => [Zou, Wenjie; Fang, Zichuan] Univ Sci & Technol Beijing, Civil & Resource Engn Sch, Beijing 100083, Peoples R China.@@@ [Huang, Jun] Shandong Univ, Key Lab High Efficiency & Clean Mech Manufacture, Ctr Adv Jet Engn Technol CaJET, Minist Educ,Sch Mech Engn, Jinan 250061, Shandong, Peoples R China.@@@ [Zhang, Zhijun] China Univ Min & Technol, Sch Chem & Environm Engn, Beijing, Peoples R China. [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073071608&doi=10.1016%2fj.colsurfa.2019.124055&partnerID=40&md5=93c6cfe638018b0af98d6c8a813396fa [tag] => 0 [author_en] => Zou, WJ; Fang, ZC; Huang, J; Zhang, ZJ [format_scopus_No] => e0dd43b732dca527dde9217296cfe02f-92289355 [format_doi] => eb5eca22d0e28a8cb305073a26e3b06e-372308797 [sys_update_time] => 2020-03-13 09:56:31 [fund_No] => National Natural Science Foundation of ChinaNational Natural Science; Foundation of China [51604019, 51905305, 51704300]; Found of State Key; Laboratory of Mineral Processing [BGRIMM-KJSKL-2017-19] [reference] => Taner, H.A., Onen, V., Control of clay minerals effect in flotation. 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B, 241 (1-4), pp. 685-688 [format_title_en_publication_en_pub_year] => 80cab64f00a271b3e2883103d40dbfb7-1750360268 [abstract_en] => Solution salinity is critical for the inhibition effect of sodium hexametaphosphate on different mineral surfaces, further affecting the separation of minerals in aqueous solution. In this work, the effect of K+ and Ca2+ on adsorption of sodium hexametaphosphate (SHMP) and hydrophobically modified polyacrylamide, poly(AM-NaAA-C(16)DMAAC) (HMPAM), on alumina surface (i.e., kaolinite Al-OH surface) were investigated using quartz crystal microbalance with dissipation (QCM-D). The deposition layer on alumina surface with the effect of adding 1 mM SHMP in 10 mM of CaCl2 solution were examined by glancing angle X-ray diffraction (GAXRD), scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). The 100 mM KCl solution promoted the adsorption of both HMPAM and SHMP on alumina surface, which would be mainly caused by the molecular configuration change. The more flexible and coiled configuration of HMPAM and SHMP in 100 mM KCl solution could be helpful for increasing the adsorption sites on alumina surface. Moreover, the intermolecular hydrophobic interaction between the alkane chains of HMPAM would lead to the multilayer adsorption of HMPAM instead of adsorbing directly onto alumina surface. Alternatively, 1 mM and 10 mM CaCl2 solutions lead to a lower adsorption amount of HMPAM on alumina surface, which is most likely caused by the suppressed electrostatic attraction and condensed conformation of HMPAM. The adsorption layer of SHMP formed on alumina surface could prevent the adsorption of HMPAM through different mechanisms in KCl and CaCl2 solutions at different concentrations. Interestingly, scattered micro-spheres were found to adsorb on alumina surface with 1 mM SHMP in 10 mM CaCl2 solution, mainly in forms of calcium hydrogen phosphate hydrate. Our work provides fundamental insights into the effect of adding dispersant (e.g., SHMP) and polymer flocculant (e.g., HMPAM) during selective flocculation of kaolinite, with helpful guidance for clay-involved mineral processing. [scopus_id] => 42962916500;57211232288;56103922300;57189222170; [from_id] => 76,74,73 [cauthor_ad] => [Huang, J]Shandong Univ, Key Lab High Efficiency & Clean Mech Manufacture, Ctr Adv Jet Engn Technol CaJET, Minist Educ,Sch Mech Engn, Jinan 250061, Shandong, Peoples R China@@@[Zhang, ZJ]China Univ Min & Technol, Sch Chem & Environm Engn, Beijing, Peoples R China. [hx_id] => 2376,2378,2371 [classification_pub] => CPEAE [doi] => 10.1016/j.colsurfa.2019.124055 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [title_en] => Effect of salinity on adsorption of sodium hexametaphosphate and hydrophobically-modified polyacrylamide flocculant on kaolinite Al-OH surface [index_keyword] => Adsorption; Alumina; Aluminum oxide; Flocculation; Hydrophobicity; Kaolinite; Multilayers; Potassium compounds; Quartz crystal microbalances; Scanning electron microscopy; Sodium compounds; X ray photoelectron spectroscopy; Alumina surface; Calcium hydrogen phosphate; Electrostatic attractions; Glancing angle x-ray diffractions; Hydrophobically modified polyacrylamides; Intermolecular hydrophobic interactions; Quartz crystal microbalance with dissipation; Sodium hexametaphosphate; Chlorine compounds; alkane; aluminum hydroxide; aluminum silicate; calcium chloride; calcium ion; calcium phosphate dibasic; hexametaphosphate sodium; polyacrylamide; adsorption; aqueous solution; Article; conformation; hydrophobicity; molecular interaction; priority journal; quartz crystal microbalance; salinity; surface property; X ray diffraction; X ray photoemission spectroscopy [volume] => 585 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Alumina surface; Adsorption; Sodium hexametaphosphate;; Hydrophobically-modified polyacrylamide [article_id] => 813374,814604,809962 [hints] => 1 [publisher] => ELSEVIER [substance] => aluminum hydroxide, 1330-44-5, 20257-20-9, 21645-51-2, 80206-84-4; aluminum silicate, 12183-80-1, 1302-93-8, 1318-74-7, 1335-30-4, 61027-90-5; calcium chloride, 10043-52-4; calcium ion, 14127-61-8; calcium phosphate dibasic, 14567-84-1, 14567-92-1, 21063-37-6, 7757-93-9, 7789-77-7; hexametaphosphate sodium, 10124-56-8, 59299-44-4; polyacrylamide, 9003-05-8 [language] => English [issn] => 0927-7757 [batch] => 3422,3418,3424 [publication_en] => COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS [email] => jun.huang@email.sdu.edu.cn; zhijunzhang@cumtb.edu.cn [document_No] => 124055 [format_title_en_issn_pub_year] => aede32de397a151abc15895fcbb899ae86468535 [publication_iso] => Colloid Surf. A-Physicochem. Eng. Asp. [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Zou, Wenjie; Fang, Zichuan; Huang, Jun; Zhang, Zhijun [ei_No] => 20194107528304 [eissn] => 1873-4359 [main_eword] => Chlorine compounds [format_publication_cn] => [format_title_en] => b34a120de9dc51be6c0c374c5a6c2da5-1487939461 [pub_date] => JAN 20 [classification_No] => 482.2 Minerals - 802.3 Chemical Operations - 804.2 Inorganic Compounds - 931.2 Physical Properties of Gases, Liquids and Solids - 943.3 Special Purpose Instruments [cauthor_order] => 3,3,4 [uncontrolled_terms] => Alumina surface - Calcium hydrogen phosphate - Electrostatic attractions - Glancing angle x-ray diffractions - Hydrophobically modified polyacrylamides - Intermolecular hydrophobic interactions - Quartz crystal microbalance with dissipation - Sodium hexametaphosphate [controlled_terms] => Adsorption - Alumina - Aluminum oxide - Flocculation - Hydrophobicity - Kaolinite - Multilayers - Potassium compounds - Quartz crystal microbalances - Scanning electron microscopy - Sodium compounds - X ray photoelectron spectroscopy [reference_No] => 37 [format_ei_No] => e45067c1258d3bb2806aea2a7a667809-1835265720 [sys_priority_field] => 73 [cauthor_back] => Huang, Jun@@@Huang, J@@@Zhang, ZJ [format_publication_en] => 28a0e71ae3db4fb412c9ff916b8bfd3e618103075 [cite_wos] => 0 [check_3Y] => 10 [delivery_No] => JV0GV [pages] => 7 [publication_29] => COLLOID SURFACE A [orcID] => Zhang, Zhijun/0000-0001-7529-9340 [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => PHOSPHATE; FLOTATION; CALCIUM; SILICATE; QUARTZ; XPS; HETEROCOAGULATION; CHALCOPYRITE; CHITOSAN; POLYMER [fund_ab] => The authors would like to thank the National Natural Science Foundation; of China (Grant No.51604019, No. 51704300and No.51905305), the Found of; State Key Laboratory of Mineral Processing (Grant No.; BGRIMM-KJSKL-2017-19). [publisher_city] => AMSTERDAM [cite_awos] => 0 [wos_No] => WOS:000502046200020 [format_wos_No] => 5fbef79714259d483fb4bd6a1f006acd-1224406809 [wos_sub] => Chemistry, Physical [research_area] => Chemistry [check_180] => 10 [publisher_ad] => RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => colloidsandsurfacesaphysicochemicalandengineeringaspects [jl_country] => 中国 [jl_keyword_en] => aluminasurface,,sodiumhexametaphosphate,hydrophobicallymodifiedpolyacrylamide,adsorption [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => elsevier [company_id] => 0,130 [author_id] => [sys_subject_sort] => 0 [college_parent_id] => 130 [company_test] => Array [id] => PAA103ABe-eYmRwwIxqf [tags] => 0 ) [17] => Array ( [standard_in] => State Key Laboratory of Heavy Oil, China University of Petroleum (East China), Qingdao, 266580, China; College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China [cauthor] => Wang, Zhenbo(dxl437@sina.com) [school_id] => 117 [scopus_No] => 2-s2.0-85070745695 [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070745695&doi=10.1016%2fj.renene.2019.06.080&partnerID=40&md5=f6d002d1d7dae18e6a7272b736d57b74 [tag] => 0 [author_en] => Gong, ZQ; Fang, PW; Wang, ZB; Li, XY; Wang, ZT; Meng, FZ [format_scopus_No] => fd0b28dc174f07dbfbef90a44961b848-1323055389 [format_doi] => de4ad140adf65954fb57105a6ec2fdea961990174 [author_in] => [Gong, Zhiqiang; Fang, Peiwen; Wang, Zhenbo; Wang, Zhentong; Meng, Fanzhi] China Univ Petr East China, State Key Lab Heavy Oil, Qingdao 266580, Shandong, Peoples R China.@@@ [Li, Xiaoyu] Shandong Univ Sci & Technol, Coll Mech & Elect Engn, Qingdao 266590, Shandong, Peoples R China. [fund_No] => Natural Science Foundation of Shandong ProvinceNatural Science; Foundation of Shandong Province [ZR2017BEE042]; Fundamental Research; Funds for the Central UniversitiesFundamental Research Funds for the; Central Universities [18CX02150A]; Talent Introduction Project of China; University of Petroleum [2017010068] [reference] => Choo, M.-Y., Oi, L.E., Show, P.L., Chang, J.-S., Ling, T.C., Ng, E.-P., Phang, S.M., Juan, J.C., Recent progress in catalytic conversion of microalgae oil to green hydrocarbon: a review (2017) J. Taiwan Institute of Chemical Engineers, 79, pp. 116-124; Yu, Y., Chua, Y.W., Wu, H., Characterization of pyrolytic sugars in bio-oil produced from biomass fast pyrolysis (2016) Energy Fuels, 30 (5), pp. 4145-4149; Ra, C.H., Kang, C.-H., Kim, N.K., Lee, C.-G., Kim, S.-K., Cultivation of four microalgae for biomass and oil production using a two-stage culture strategy with salt stress (2015) Renew. Energy, 80, pp. 117-122; Show, K.Y., Yan, Y., Ling, M., Ye, G., Li, T., Lee, D.J., Hydrogen production from algal biomass - advances, challenges and prospects (2018) Bioresour. Technol., 257, pp. 290-300; Bilgili, F., Koçak, E., Bulut, Ü., Kuşkaya, S., Can biomass energy be an efficient policy tool for sustainable development? (2017) Renew. Sustain. Energy Rev., 71, pp. 830-845; Chew, K.W., Chia, S.R., Show, P.L., Yap, Y.J., Ling, T.C., Chang, J.-S., Effects of water culture medium, cultivation systems and growth modes for microalgae cultivation: a review (2018) Journal of the Taiwan Institute of Chemical Engineers, 91, pp. 332-344; Colom-Díaz, J.M., Alzueta, M.U., Fernandes, U., Costa, M., Emissions of Polycyclic Aromatic Hydrocarbons during Biomass Combustion in a Drop Tube Furnace, Fuel (2017); Durak, H., Aysu, T., Thermochemical liquefaction of algae for bio-oil production in supercritical acetone/ethanol/isopropanol (2016) J. Supercrit. Fluids, 111, pp. 179-198; Hanifzadeh, M., Garcia, E.C., Viamajala, S., Production of lipid and carbohydrate from microalgae without compromising biomass productivities: role of Ca and Mg (2018) Renew. Energy, 127, pp. 989-997; Molinuevo-Salces, B., Mahdy, A., Ballesteros, M., González-Fernández, C., From piggery wastewater nutrients to biogas: microalgae biomass revalorization through anaerobic digestion (2016) Renew. Energy, 96, pp. 1103-1110; Wang, X., Guo, F., Li, Y., Yang, X., Effect of pretreatment on microalgae pyrolysis: kinetics, biocrude yield and quality, and life cycle assessment (2017) Energy Convers. Manag., 132, pp. 161-171; Changi, S.M., Faeth, J.L., Mo, N., Savage, P.E., Hydrothermal reactions of biomolecules relevant for microalgae liquefaction (2015) Ind. Eng. Chem. 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Technol., 109, pp. 163-172; Sanchez-Silva, L., Lopez-Gonzalez, D., Garcia-Minguillan, A.M., Valverde, J.L., Pyrolysis, combustion and gasification characteristics of Nannochloropsis gaditana microalgae (2013) Bioresour. Technol., 130, pp. 321-331; Bach, Q.V., Chen, W.H., Pyrolysis characteristics and kinetics of microalgae via thermogravimetric analysis (TGA): a state-of-the-art review (2017) Bioresour. Technol., 246, pp. 88-100; Gong, Z., Du, A., Wang, Z., Fang, P., Li, X., Experimental study on pyrolysis characteristics of oil sludge with a tube furnace reactor (2017) Energy Fuels, 31 (8), pp. 8102-8108; Li, D., Chen, L., Chen, S., Zhang, X., Chen, F., Ye, N., Comparative evaluation of the pyrolytic and kinetic characteristics of a macroalga (Sargassum thunbergii) and a freshwater plant (Potamogeton crispus) (2012) Fuel, 96, pp. 185-191 [format_title_en_publication_en_pub_year] => 7f798ef238a2ee8e13183c93ced0b4c0-1634143767 [abstract_en] => Pyrolysis characteristics of microalgae (MA) and its chemical extraction residue (MR) were evaluated and compared using a thermal analyzer and a tube furnace reactor. Results showed that the pyrolysis process of MA and MR can be divided into three stages, which corresponded to the volatile of free water, the decomposition of organic compounds and the stabilization of residues, respectively. Due to the removal of lipids after MA extraction, only one weight loss peak was recorded in the second stage of MR. Using the methods of Friedman, FWO and Starink, the average activation energies of MA and MR were calculated as 204.72 and 178.51 kJ/mol, respectively. Pyrolysis oil, gas, and char products were obtained from both MA and MR pyrolysis. Main gas products from pyrolysis of MA and MR contained CO2, CO, H-2 and CHs. Compared with MA pyrolysis, the relative contents of CHs were lower (<59%) during MR pyrolysis, but the contents of CH4 were higher. Results for higher hydrocarbons (C4-6, C6+) were lower in MR than in MA pyrolysis. MR pyrolysis can be a promising method for the waste treatment with high value-added pyrolysis liquid and gases products. (C) 2019 Elsevier Ltd. All rights reserved. [scopus_id] => 29467476500;57195401627;55719793100;57192491284;57196095028;57202301960; [from_id] => 76,74,73 [cauthor_ad] => [Wang, ZB]China Univ Petr East China, State Key Lab Heavy Oil, Qingdao 266580, Shandong, Peoples R China. [hx_id] => 2376,2378,2371 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [title_en] => Pyrolysis characteristics and products distribution of haematococcus pluvialis microalgae and its extraction residue [index_keyword] => Activation energy; Algae; Extraction; Microorganisms; Thermoanalysis; Waste treatment; Chemical extractions; Decomposition of organic compounds; Haematococcus pluvialis; Kinetic analysis; Micro-algae; Products distributions; Pyrolysis characteristics; Pyrolysis products; Pyrolysis; decomposition; equipment; extraction method; hydrocarbon; microalga; pyrolysis; reaction kinetics; stabilization; waste treatment; Haematococcus pluvialis [volume] => 146 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Pyrolysis; Kinetic analysis; Microalgae; Microalgae residue; Pyrolysis; products [article_id] => 813554,819750,808346 [begin_page] => 2134 [hints] => 1 [publisher] => PERGAMON-ELSEVIER SCIENCE LTD [doi] => 10.1016/j.renene.2019.06.080 [language] => English [issn] => 0960-1481 [batch] => 3422,3418,3424 [publication_en] => RENEWABLE ENERGY [email] => dx1437@sina.com [sys_update_time] => 2020-03-13 09:56:09 [format_title_en_issn_pub_year] => 3862541ce68755ebcec4c344375ef5d71333859527 [publication_iso] => Renew. Energy [SYS_TAG] => 3 [end_page] => 2141 [page] => 2134-2141 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [format_title] => [author_fn] => Gong, Zhiqiang; Fang, Peiwen; Wang, Zhenbo; Li, Xiaoyu; Wang, Zhentong; Meng, Fanzhi [ei_No] => 20193407333201 [eissn] => 18790682 [main_eword] => Pyrolysis [format_publication_cn] => [format_title_en] => 8d64a5af49e901140d6bac45e747aabd-1339622341 [pub_date] => FEB [classification_No] => 452.4 Industrial Wastes Treatment and Disposal - 461.9 Biology - 801 Chemistry - 802.2 Chemical Reactions - 802.3 Chemical Operations [cauthor_order] => 3,3 [uncontrolled_terms] => Chemical extractions - Decomposition of organic compounds - Haematococcus pluvialis - Kinetic analysis - Micro-algae - Products distributions - Pyrolysis characteristics - Pyrolysis products [controlled_terms] => Activation energy - Algae - Extraction - Microorganisms - Thermoanalysis - Waste treatment [reference_No] => 35 [format_ei_No] => 005f03c1297ac0ef946019f88933ad5f2001181802 [sys_priority_field] => 73 [cauthor_back] => Wang, Zhenbo@@@Wang, ZB [format_publication_en] => 64f16cd4edccdceb5fee5dd8533d6b08-702297104 [cite_wos] => 0 [check_3Y] => 16 [delivery_No] => JR6WD [pages] => 8 [publication_29] => RENEW ENERG [orcID] => Gong, Zhiqiang/0000-0001-5795-257X [publication_type] => J [get_data] => 2020-03-06 [keyword_plu] => CO-PYROLYSIS; OIL PRODUCTION; BIOMASS; COMBUSTION; KINETICS; SLUDGE; CULTIVATION; COMPONENTS [fund_ab] => The research was supported by the Natural Science Foundation of Shandong; Province (No. ZR2017BEE042), the Fundamental Research Funds for the; Central Universities (NO. 18CX02150A), and the Talent Introduction; Project of China University of Petroleum (No. 2017010068). [publisher_city] => OXFORD [cite_awos] => 0 [wos_No] => WOS:000499762300058 [format_wos_No] => 62ec129d5016516fc5615a565a45584f-138411507 [wos_sub] => Green & Sustainable Science & Technology; Energy & Fuels [research_area] => Science & Technology - Other Topics; Energy & Fuels [check_180] => 16 [publisher_ad] => THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => renewableenergy [jl_country] => 中国 [jl_keyword_en] => pyrolysis,microalgaeresidue,kineticanalysis,products,microalgae [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => pergamonelsevierscienceltd [company_id] => 0,0 [author_id] => [id] => jQA003ABe-eYmRww6g7I [tags] => 0 ) [18] => Array ( [standard_in] => School of Chemistry and Chemical Engineering, Shandong University of Technology, Zibo, 255000, China [cauthor] => Sun, X(sunxiuyu@sdut.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85077693980 [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077693980&doi=10.1002%2fslct.201903959&partnerID=40&md5=04d4dfc14cef6d056c4e64328662e6ac [tag] => 0 [author_en] => Li, CX; Wang, Y; Zhang, Y; Wang, M; Sun, XY; Cui, HY; Xie, YJ [format_scopus_No] => c9bb58f3b39aae5ee6792e3814e71b7f930390 [format_doi] => 9313da6cc455262d7d3d8aec085e7df2-1556989900 [author_in] => [Li, Chunxiao; Wang, Yong; Zhang, Yuan; Wang, Ming; Sun, Xiuyu; Cui, Hongyou; Xie, Yujiao] Shandong Univ Technol, Sch Chem & Chem Engn, Zibo 255000, Peoples R China. [fund_No] => National Natural Science Foundations of ChinaNational Natural Science; Foundation of China [21978158, 21476132]; Public Welfare Category of Key; R&D Programs in Shandong Province [2018GGX107003]; Natural Science; Foundation of Shandong ProvinceNatural Science Foundation of Shandong; Province [ZR2018BB062] [reference] => Acharjee, T.C., Lee, Y.Y., (2018) Environ. Prog. Sustain. Energy, 37, pp. 471-480; Zhang, X., Murria, P., Jiang, Y., Xiao, W., Kenttämaa, H.I., Abu-Omar, M.M., Mosier, N.S., (2016) Green Chem., 18, pp. 5219-5229; Atanda, L., Mukundan, S., Shrotri, A., Ma, Q., Beltramini, J., (2015) ChemCatChem, 7, pp. 781-790; Osatiashtiani, A., Lee, A.F., Granollers, M., Brown, D.R., Olivi, L., Morales, G., Melero, J.A., Wilson, K., (2015) ACS Catal., 5, pp. 4345-4352; Guo, J., Zhu, S., Cen, Y., Qin, Z., Wang, J., Fan, W., (2017) Appl. Catal. 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Mater. Sci., 52, pp. 628-642; Delidovich, I., Palkovits, R., (2014) Catal. Sci. Technol., 4, pp. 4322-4329; Lee, G., Jeong, Y., Takagaki, A., Jung, J.C., (2014) J. Mol. Catal. A: Chem., 393, pp. 289-295; Yabushita, M., Shibayama, N., Nakajima, K., Fukuoka, A., (2019) ACS Catal., 9, pp. 2101-2109; Carraher, J.M., Fleitman, C.N., Tessonnier, J.P., (2015) ACS Catal., 5, pp. 3162-3173; Moreau, C., Durand, R., Roux, A., Tichit, D., (2000) Appl. Catal., A, 193, pp. 257-264; Yang, Q., Sherbahn, M., Runge, T., (2016) ACS Sustain. Chem. Eng., 4, pp. 3526-3534 [format_title_en_publication_en_pub_year] => 6b0f59ab2aecfe3452e76d244c3cb388-1392747783 [abstract_en] => Mg-Al hydrotalcites for catalyzing glucose isomerization to fructose are prepared. Effects of atom ratios of Mg/Al ranging from 1 to 4 and calcination temperatures from 250-650 degrees C on catalytic performance were examined systematically. It was found that the hydrotalcite (Mg2Al1LDH) calcined at 450 degrees C with an atom ratio of Mg/Al=2 is an excellent catalyst for glucose isomerization. Characterizations by SEM, EDS, TEM, XRD, FTIR, TG and BET demonstrated that the formed oxide condensed phase as well as the interaction with the alumina helps promote glucose isomerization reaction due to their appropriate basic sites at high calcination temperature. With Mg2Al1-450 as the catalyst, kinetic model analysis confirmed that the reaction of glucose isomerization to fructose was an exothermal reversible reaction; both the forward and reverse reactions are quasi-first-order reactions with activation energies of 71.82 kJ/mol and 48.15 kJ/mol, respectively, while the side reaction is a zero-order reaction with an activation energy of 94.22 kJ/mol. Therefore, high isomerization reaction temperature is not beneficial to enhance the yield and selectivity of fructose. [scopus_id] => 57213185898;56539669500;57192653491;57208171446;8393778700;35195439800;56377266700; [from_id] => 76,73 [cauthor_ad] => [Sun, XY; Cui, HY]Shandong Univ Technol, Sch Chem & Chem Engn, Zibo 255000, Peoples R China. [hx_id] => 2378,2371 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11 [format_issn_issue_page_pub_year] => 775a85e2466f996b376b7a8931ac2d23-304399577 [title_en] => Isomerization Kinetics of Glucose to Fructose in Aqueous Solution with Magnesium-Aluminum Hydrotalcites [volume] => 5 [source_type] => 351 [pub_year] => 2020 [keyword_en] => fructose; glucose; isomerization; kinetics; Mg-Al hydrotalcites [article_id] => 813069,814475 [begin_page] => 270 [hints] => 0 [publisher] => WILEY-V C H VERLAG GMBH [doi] => 10.1002/slct.201903959 [language] => English [issue] => 1 [issn] => 2365-6549 [batch] => 3422,3424 [publication_en] => CHEMISTRYSELECT [email] => sunxiuyu@sdut.edu.cn; cuihy@sdut.edu.cn [sys_update_time] => 2020-03-13 09:56:31 [format_title_en_issn_pub_year] => 34769bf0c832f19b2faefcb2b13ae5d5-1668207036 [publication_iso] => ChemistrySelect [SYS_TAG] => 3 [end_page] => 279 [page] => 270-279 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [cite_wos] => 0 [check_3Y] => 1 [delivery_No] => KB3ZU [format_title] => [author_fn] => Li, Chunxiao; Wang, Yong; Zhang, Yuan; Wang, Ming; Sun, Xiuyu; Cui, Hongyou; Xie, Yujiao [pages] => 10 [publication_29] => CHEMISTRYSELECT [open_type] => Bronze [orcID] => Cui, Hongyou/0000-0003-4577-3835 [publication_type] => J [get_data] => 2020-03-06 [format_publication_cn] => [keyword_plu] => SOLID BASE CATALYSTS; MG-AL HYDROTALCITES; HETEROGENEOUS CATALYST; LEVULINIC ACID; CONVERSION; CONDENSATION; DEGRADATION; REACTIVITY; ISOMERASE; BASICITY [fund_ab] => The project was supported by the National Natural Science Foundations of; China (21978158 and 21476132), the Public Welfare Category of Key R&D; Programs in Shandong Province (2018GGX107003) and the Natural Science; Foundation of Shandong Province (ZR2018BB062). [format_title_en] => 5989b7efc00babdac04033de5777a1e1-721710148 [publisher_city] => WEINHEIM [pub_date] => JAN 9 [cauthor_order] => 5,6 [reference_No] => 48 [cite_awos] => 0 [wos_No] => WOS:000506438700035 [sys_priority_field] => 73 [format_wos_No] => 6dc1b0c82d458574150f25adbc3adda7-2142969944 [wos_sub] => Chemistry, Multidisciplinary [research_area] => Chemistry [cauthor_back] => Sun, XY@@@Cui, HY [check_180] => 1 [publisher_ad] => POSTFACH 101161, 69451 WEINHEIM, GERMANY [format_publication_en] => fad3620dbf54941f092c5fbf63ab844b1747514688 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => chemistryselect [jl_country] => 中国 [jl_keyword_en] => isomerization,glucose,kinetics,mgalhydrotalcites,fructose [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => wileyvchverlaggmbh [company_id] => 0,0 [author_id] => [id] => uwA103ABe-eYmRwwIxmf [tags] => 0 ) [19] => Array ( [standard_in] => Institute for Financial Studies, School of Mathematics, Shandong University, Jinan, 250100, China; Department of Mathematical Sciences, Loughborough University, Loughborough, LE11 3TU, United Kingdom [cauthor] => Shi, Y(yfshi@sdu.edu.cn) [school_id] => 117 [scopus_No] => 2-s2.0-85077070291 [batch2] => 15 [uri] => https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077070291&doi=10.1016%2fj.jmaa.2019.123791&partnerID=40&md5=7aac48e622c5e309a902017bf676e440 [tag] => 0 [author_en] => Shi, YF; Zhao, HZ [format_scopus_No] => 883cc9206a29fc2e20bc0081b9d1c58b429600498 [format_doi] => 34cf73e0180c8afb1a9c334a9d3fad3e-604936485 [sys_update_time] => 2020-03-13 09:56:08 [fund_No] => National Key R&D Program of China [2018YFA0703900]; National Natural; Science Foundation of ChinaNational Natural Science Foundation of China; [11871309, 11371226]; Royal Society Newton Advanced Fellowship; [NA150344] [reference] => Antonelli, F., Backward-forward stochastic differential equations (1993) Ann. Appl. Probab., 3, pp. 777-793; Barles, G., Buckdahn, R., Pardoux, E., BSDEs and integral-partial differential equations (1997) Stochastics, 60, pp. 57-83; Crandall, M.G., Ishii, H., Lions, P.L., User\'s guide to viscosity solutions of second order partial differential equations (1992) Bull. Amer. Math. Soc., 27, pp. 1-67; Cvitanic, J., Ma, J., Hedging options for a large investor and forward-backward stochastic differential equations (1996) Ann. Appl. Probab., 6, pp. 370-398; Darling, R., Pardoux, E., Backward SDE with random terminal time and applications to semilinear elliptic PDE (1997) Ann. Probab., 25, pp. 1135-1159; El Karoui, N., Peng, S.G., Quenez, M.C., Backward stochastic differential equation in finance (1997) Math. Finance, 7, pp. 1-71; Feng, C.R., Wang, X.C., Zhao, H.Z., Quasi-linear PDEs and forward-backward stochastic differential equations: weak solutions (2018) J. Differential Equations, 264, pp. 959-1018; Fleming, W.H., Soner, H.M., Controlled Markov Processes and Viscosity Solutions (1993), Springer-Verlag New York; Hu, Y., Peng, S.G., Solution of forward-backward stochastic differential equations (1995) Probab. Theory Related Fields, 103, pp. 273-283; Ma, J., Protter, P., Yong, J.M., Solving forward-backward stochastic differential equations explicitly—a four step scheme (1994) Probab. Theory Related Fields, 98, pp. 339-359; Ma, J., Wu, Z., Zhang, D.T., Zhang, J.F., On wellposedness of forward-backward SDEs-a unified approach (2015) Ann. Appl. Probab., 25, pp. 2168-2214; Ma, J., Yong, J.M., Forward-Backward Stochastic Differential Equations and Their Applications (1999) Lecture Notes in Math., 1702. , Springer-Verlag Berlin; Pardoux, E., Backward stochastic differential equations and viscosity solutions of systems of semilinear parabolic and elliptic PDEs of second order (1998) Stochastic Analysis and Related Topics VI: The Geilo Workshop, pp. 79-127. , L. Decreusefond J. Gjerde B. Oksendal A.S. Ustunel 1996 Birkhäuser; Pardoux, E., Peng, S.G., Adapted solution of a backward stochastic differential equation (1990) Systems Control Lett., 14, pp. 55-61; Pardoux, E., Peng, S.G., Backward stochastic differential equations and quasilinear parabolic partial differential equations (1992) Stochastic Partial Differential Equations and Their Applications, Lect. Notes Control Inf. Sci., 176, pp. 200-217. , B.L. Rozuvskii R.B. Sowers Springer Berlin, Heidelberg, New York; Pardoux, E., Peng, S.G., Backward doubly stochastic differential equations and systems of quasilinear parabolic SPDEs (1994) Probab. Theory Related Fields, 98, pp. 209-227; Pardoux, E., Tang, S.J., Forward-backward stochastic differential equations and quasilinear parabolic PDEs (1999) Probab. Theory Related Fields, 114, pp. 123-150; Pardoux, E., Veretennikov, A.Y., On the Poisson equation and diffusion approximation I (2001) Ann. Probab., 29, pp. 1061-1085; Pardoux, E., Veretennikov, A.Y., On the Poisson equation and diffusion approximation II (2003) Ann. Probab., 31, pp. 1166-1192; Pardoux, E., Veretennikov, A.Y., On the Poisson equation and diffusion approximation III (2005) Ann. Probab., 33, pp. 1111-1133; Peng, S.G., Probabilistic interpretation for systems of quasilinear parabolic partial differential equations (1991) Stochastics, 37, pp. 61-74; Peng, S.G., Shi, Y.F., Infinite horizon forward-backward stochastic differential equations (2000) Stochastic Process. Appl., 85, pp. 75-92; Peng, S.G., Wu, Z., Fully coupled forward-backward stochastic differential equations and applications to optimal control (1999) SIAM J. Control Optim., 37, pp. 825-843; Yong, J.M., Finding adapted solutions of forward-backward stochastic differential equations—method of continuation (1997) Probab. Theory Related Fields, 107, pp. 537-572; Yu, Z.Y., Infinite horizon jump-diffusion forward-backward stochastic differential equations and their application to backward linear-quadratic problems (2017) ESAIM Control Optim. Calc. Var., 23, pp. 1331-1359; Zhang, Q., Zhao, H.Z., Pathwise stationary solutions of stochastic partial differential equations and backward doubly stochastic differential equations on infinite horizon (2007) J. Funct. Anal., 252, pp. 171-219; Zhang, Q., Zhao, H.Z., SPDEs with polynomial growth coefficients and Malliavin calculus method (2013) Stochastic Process. Appl., 123, pp. 2228-2271 [format_title_en_publication_en_pub_year] => 1202381420a722d5cc745b01bfd0236a1163446448 [abstract_en] => A class of infinite horizon forward-backward stochastic differential equations (FBSDEs) is investigated. Under some monotonicity conditions, the existence and uniqueness of solutions in an arbitrarily large space for FBSDEs on infinite horizon is obtained. The probabilistic interpretations for a large class of quasilinear elliptic partial differential equations (PDEs) in a global space is then given by virtue of the solutions of FBSDEs on infinite horizon. (C) 2019 Elsevier Inc. All rights reserved. [scopus_id] => 55495655200;7404778984; [from_id] => 76,73 [cauthor_ad] => [Shi, YF]Shandong Univ, Inst Financial Studies, Jinan 250100, Peoples R China@@@[Shi, YF]Shandong Univ, Sch Math, Jinan 250100, Peoples R China. [hx_id] => 2378,2371 [datebase] => Scopus [sys_level_num] => 15_6 [sys_jg_type] => 11,3 [title_en] => Forward-backward stochastic differential equations on infinite horizon and quasilinear elliptic PDEs [author_in] => [Shi, Yufeng] Shandong Univ, Inst Financial Studies, Jinan 250100, Peoples R China.@@@ [Shi, Yufeng] Shandong Univ, Sch Math, Jinan 250100, Peoples R China.@@@ [Zhao, Huaizhong] Loughborough Univ, Dept Math Sci, Loughborough LE11 3TU, Leics, England. [volume] => 485 [source_type] => 351 [pub_year] => 2020 [keyword_en] => Forward-backward stochastic differential equations; Elliptic PDEs;; Probabilistic interpretations; Feynman-Kac formula; Poisson equations [article_id] => 810705,820165 [hints] => 0 [publisher] => ACADEMIC PRESS INC ELSEVIER SCIENCE [doi] => 10.1016/j.jmaa.2019.123791 [language] => English [issue] => 10 [issn] => 0022-247X [batch] => 3422,3424 [publication_en] => JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS [email] => yfshi@sdu.edu.cn; H.Zhao@lboro.ac.uk [document_No] => 123791 [format_title_en_issn_pub_year] => 750a7aefe4521358e525fac50e9f9a6f-2058533530 [publication_iso] => J. Math. Anal. Appl. [SYS_TAG] => 3 [hb_type] => 2 [article_dt] => Article [hb_batch] => grant_no [cite_wos] => 0 [check_3Y] => 0 [delivery_No] => KF7LD [format_title] => [author_fn] => Shi, Yufeng; Zhao, Huaizhong [pages] => 19 [publication_29] => J MATH ANAL APPL [eissn] => 1096-0813 [publication_type] => J [get_data] => 2020-03-06 [format_publication_cn] => [keyword_plu] => DIFFUSION-APPROXIMATION; POISSON-EQUATION; SPDES [fund_ab] => Y. Shi is partially supported by the National Key R&D Program of China; (Grant No. 2018YFA0703900), the National Natural Science Foundation of; China (Grant Nos. 11871309 and 11371226). H. Zhao is partially supported; by the Royal Society Newton Advanced Fellowship NA150344. [format_title_en] => bfa8d6f2fafa47fa7dfad3987578ac5b-2095758037 [publisher_city] => SAN DIEGO [pub_date] => MAY 1 [cauthor_order] => 1,1 [reference_No] => 27 [cite_awos] => 0 [wos_No] => WOS:000509419800018 [sys_priority_field] => 73 [format_wos_No] => 74c99eed6365aa183cf216536358a293684467206 [wos_sub] => Mathematics, Applied; Mathematics [research_area] => Mathematics [cauthor_back] => Shi, YF@@@Shi, YF [check_180] => 0 [publisher_ad] => 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA [format_publication_en] => 5743e727f3d5339d2e614299bf008506-498152162 [jl_language] => english [jl_article_dt] => 期刊论文 [jl_publication_en] => journalofmathematicalanalysisandapplications [jl_country] => 中国 [jl_keyword_en] => ,forwardbackwardstochasticdifferentialequations,poissonequations,probabilisticinterpretations,feynmankacformula,ellipticpdes [sys_author_in_last_arr] => peoplesrchina [jl_publisher] => academicpressincelsevierscience [author_test] => Array ( [0] => Array ( [sure] => 1 [irmagnum] => 0 [u_index] => 0 [name] => 石玉峰 [sys_author_id] => Array ( [0] => 23299 ) [irtag] => 0 [t_index] => 0 [person_id] => 23299 ) ) [company_id] => 0,156 [author_id] => 23299 [sys_subject_sort] => 0 [college_parent_id] => 156 [company_test] => Array [id] => kAA003ABe-eYmRwwyAaA [tags] => 0 ) ) 1-->
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11. Multistage Impact Energy Distribution for Whole Vehicles in High-Speed Train Collisions: Modeling and Solution Methodology EI SCOPUS SCIE

作者:Zhang, Honghao; Peng, Yong; Hou, Lin; Wang, Danqi; Tian, Guangdong; Li, Zhiwu

作者机构:[Zhang, Honghao; Peng, Yong; Hou, Lin] Cent S Univ, Minist Educ, Key Lab Traff Safety Track, Sch Traff & Transportat Engn, Changsha 410000, Peoples R China.; [Wang, Danqi] Jilin Univ, Coll Automot Engn, Changchun 130025, Peoples R China.; [Tian, Guangdong] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China.; [Li, Zhiwu] Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China.; [Li, Zhiwu] Xidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China.

来源:IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,Vol.16,Issue.4,2486-2499

WOS被引数:2

资源类型:期刊论文

WOS:000510901000030

12. Development of a new controlled low strength filling material from the activation of copper slag: Influencing factors and mechanism analysis EI SCOPUS SCIE

作者:Lan, Wentao; Wu, Aixiang; Yu, Ping

作者机构:[Lan, Wentao; Wu, Aixiang] Univ Sci & Technol Beijing, Sch Civil & Environm Engn, Beijing 100083, Peoples R China.; [Lan, Wentao; Wu, Aixiang] Univ Sci & Technol Beijing, Minist Educ, Key Lab High Efficient Min & Safety Met Mines, Beijing 100083, Peoples R China.; [Yu, Ping] ShanDong Univ Technol, Sch Resource & Environm Engn, Zibo 255049, Peoples R China.

来源:JOURNAL OF CLEANER PRODUCTION,2020,Vol.246

资源类型:期刊论文

WOS:000504632600103

13. Silica of varied pore sizes as supports of copper catalysts for hydrogenation of furfural and phenolics: Impacts of steric hindrance EI SCOPUS SCIE

作者:Yu, Zhenjie; Zhang, Lijun; Zhang, Zhanming; Zhang, Shu; Hu, Song; Xiang, Jun; Wang, Yi; Liu, Qing; Liu, Qianhe; Hu, Xun

作者机构:[Yu, Zhenjie; Zhang, Lijun; Zhang, Zhanming; Liu, Qianhe; Hu, Xun] Univ Jinan, Sch Mat Sci & Engn, Jinan 250022, Peoples R China.; [Zhang, Shu] Nanjing Forestry Univ, Coll Mat Sci & Engn, Nanjing 210037, Jiangsu, Peoples R China.; [Hu, Song; Xiang, Jun; Wang, Yi] Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan 430074, Peoples R China.; [Liu, Qing] Shandong Univ Sci & Technol, Coll Chem & Environm Engn, Key Lab Low Carbon Energy & Chem Engn, Qingdao 266590, Shandong, Peoples R China.

来源:INTERNATIONAL JOURNAL OF HYDROGEN ENERGY,2020,Vol.45,Issue.4,2720-2728

资源类型:期刊论文

WOS:000513294900023

14. Energy-size reduction of mixtures of anthracite and coking coal in Hardgrove mill EI SCOPUS SCIE

作者:Lu, Qichang; Xie, Weining; Zhang, Fengbin; He, Yaqun; Duan, Chenlong; Wang, Shuai; Zhu, Xiangnan

作者机构:[Lu, Qichang; Zhang, Fengbin; He, Yaqun; Duan, Chenlong] China Univ Min & Technol, Sch Chem Engn & Technol, Xuzhou 221116, Jiangsu, Peoples R China.; [Xie, Weining; He, Yaqun; Wang, Shuai] China Univ Min & Technol, Adv Anal & Computat Ctr, Xuzhou 221116, Jiangsu, Peoples R China.; [Xie, Weining] Jiangsu Huahong Technol Stock Ltd Co, Wuxi 214400, Jiangsu, Peoples R China.; [Zhu, Xiangnan] Shandong Univ Sci & Technol, Sch Chem & Environm Engn, Qingdao 266590, Shandong, Peoples R China.

来源:FUEL,2020,Vol.264

资源类型:期刊论文

WOS:000505667000074

15. Improved fuzzy C-means algorithm based on density peak EI SCIE

作者:Liu, Xiang-yi; Fan, Jian-cong; Chen, Zi-wen

作者机构:[Liu, Xiang-yi; Fan, Jian-cong; Chen, Zi-wen] Shandong Univ Sci, Coll Comp Sci, Engn, Technology, Qingdao, Peoples R China.; [Fan, Jian-cong] Shandong Univ Sci, Prov Key Lab Informat Technol Wisdom Min Shandong, Technology, Qingdao, Peoples R China.; [Fan, Jian-cong] Shandong Univ Sci, Prov Expt Teaching Demonstrat Ctr Comp, Technology, Qingdao, Peoples R China.

来源:INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2020,Vol.11,Issue.3,545-552

资源类型:期刊论文

WOS:000513283300004

16. Precession-scale climate forcing of peatland wildfires during the early middle Jurassic greenhouse period EI SCOPUS SCIE

作者:Zhang, Zhihui; Wang, Chengshan; Lv, Dawei; Hay, William W.; Wang, Tiantian; Cao, Shuo

作者机构:[Zhang, Zhihui; Wang, Chengshan; Wang, Tiantian; Cao, Shuo] China Univ Geosci, State Key Lab Biogeol & Environm Geol, Beijing 100083, Peoples R China.; [Zhang, Zhihui; Wang, Chengshan; Lv, Dawei; Cao, Shuo] China Univ Geosci, Sch Earth Sci & Resources, Beijing 100083, Peoples R China.; [Lv, Dawei] Shandong Univ Sci & Technol, Shandong Prov Key Lab Deposit Mineralizat & Sedim, Qingdao 266590, Shandong, Peoples R China.; [Hay, William W.] Univ Colorado Boulder, Dept Geol Sci, 2045 Windcliff Dr, Estes Pk, CO 80517 USA.; [Wang, Tiantian] China Univ Geosci, Inst Earth Sci, Beijing 100083, Peoples R China.

来源:GLOBAL AND PLANETARY CHANGE,2020,Vol.184

资源类型:期刊论文

WOS:000508491500008

17. Effect of salinity on adsorption of sodium hexametaphosphate and hydrophobically-modified polyacrylamide flocculant on kaolinite Al-OH surface EI SCOPUS SCIE

作者:Zou, Wenjie; Fang, Zichuan; Huang, Jun; Zhang, Zhijun

作者机构:[Zou, Wenjie; Fang, Zichuan] Univ Sci & Technol Beijing, Civil & Resource Engn Sch, Beijing 100083, Peoples R China.; [Huang, Jun] Shandong Univ, Key Lab High Efficiency & Clean Mech Manufacture, Ctr Adv Jet Engn Technol CaJET, Minist Educ,Sch Mech Engn, Jinan 250061, Shandong, Peoples R China.; [Zhang, Zhijun] China Univ Min & Technol, Sch Chem & Environm Engn, Beijing, Peoples R China.

来源:COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS,2020,Vol.585

资源类型:期刊论文

WOS:000502046200020

18. Pyrolysis characteristics and products distribution of haematococcus pluvialis microalgae and its extraction residue EI SCOPUS SCIE

作者:Gong, Zhiqiang; Fang, Peiwen; Wang, Zhenbo; Li, Xiaoyu; Wang, Zhentong; Meng, Fanzhi

作者机构:[Gong, Zhiqiang; Fang, Peiwen; Wang, Zhenbo; Wang, Zhentong; Meng, Fanzhi] China Univ Petr East China, State Key Lab Heavy Oil, Qingdao 266580, Shandong, Peoples R China.; [Li, Xiaoyu] Shandong Univ Sci & Technol, Coll Mech & Elect Engn, Qingdao 266590, Shandong, Peoples R China.

来源:RENEWABLE ENERGY,2020,Vol.146,2134-2141

资源类型:期刊论文

WOS:000499762300058

19. Isomerization Kinetics of Glucose to Fructose in Aqueous Solution with Magnesium-Aluminum Hydrotalcites SCOPUS SCIE

作者:Li, Chunxiao; Wang, Yong; Zhang, Yuan; Wang, Ming; Sun, Xiuyu; Cui, Hongyou; Xie, Yujiao

作者机构:[Li, Chunxiao; Wang, Yong; Zhang, Yuan; Wang, Ming; Sun, Xiuyu; Cui, Hongyou; Xie, Yujiao] Shandong Univ Technol, Sch Chem & Chem Engn, Zibo 255000, Peoples R China.

来源:CHEMISTRYSELECT,2020,Vol.5,Issue.1,270-279

资源类型:期刊论文

WOS:000506438700035

20. Forward-backward stochastic differential equations on infinite horizon and quasilinear elliptic PDEs SCOPUS SCIE

作者:Shi, Yufeng; Zhao, Huaizhong

作者机构:[Shi, Yufeng] Shandong Univ, Inst Financial Studies, Jinan 250100, Peoples R China.; [Shi, Yufeng] Shandong Univ, Sch Math, Jinan 250100, Peoples R China.; [Zhao, Huaizhong] Loughborough Univ, Dept Math Sci, Loughborough LE11 3TU, Leics, England.

来源:JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS,2020,Vol.485,Issue.10

资源类型:期刊论文

WOS:000509419800018

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