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Jie Lu

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Lu, J. & Teulings, C., 2016. "Falling Real Interest Rates, House Prices, and the Introduction of the Pill," Cambridge Working Papers in Economics 1662, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Probst, Julius, 2019. "Global real interest rate dynamics from the late 19th century to today," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 522-547.
    2. Jacopo Bonchi & Giacomo Caracciolo, 2021. "Declining natural interest rate in the US: the pension system matters," Temi di discussione (Economic working papers) 1317, Bank of Italy, Economic Research and International Relations Area.

  2. Yi Zhang & Douglas K. R. Robinson & Alan L. Porter & Donghua Zhu & Guangquan Zhang & Jie Lu, 2015. "Technology roadmapping for competitive technical intelligence," Post-Print hal-01276909, HAL.

    Cited by:

    1. Landoni, Matteo & ogilvie, dt, 2019. "Convergence of innovation policies in the European aerospace industry (1960–2000)," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 174-184.
    2. Ogden, Joan & Jaffe, Amy Myers & Scheitrum, Daniel & McDonald, Zane & Miller, Marshall, 2018. "Natural gas as a bridge to hydrogen transportation fuel: Insights from the literature," Energy Policy, Elsevier, vol. 115(C), pages 317-329.
    3. Marek Jemala, 2019. "Problematic Roadmapping for Companies in Less Developed Regions of Slovakia," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(08), pages 1-26, December.
    4. Azimi, Sasan & Rahmani, Rouhollah & Fateh-rad, Mahdi, 2019. "Investment cost optimization for industrial project portfolios using technology mining," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 243-253.
    5. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    6. Grinin, Leonid E. & Grinin, Anton L. & Korotayev, Andrey, 2017. "Forthcoming Kondratieff wave, Cybernetic Revolution, and global ageing," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 52-68.
    7. de Alcantara, Douglas Pedro & Martens, Mauro Luiz, 2019. "Technology Roadmapping (TRM): a systematic review of the literature focusing on models," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 127-138.
    8. Kim, Junhan & Geum, Youngjung, 2021. "How to develop data-driven technology roadmaps:The integration of topic modeling and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    9. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    10. Nayak, Bishwajit & Bhattacharyya, Som Sekhar & Krishnamoorthy, Bala, 2021. "Explicating the role of emerging technologies and firm capabilities towards attainment of competitive advantage in health insurance service firms," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    11. Yuskevich, Ilya & Hein, Andreas Makoto & Amokrane-Ferka, Kahina & Doufene, Abdelkrim & Jankovic, Marija, 2021. "A metamodel of an informational structure for model-based technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Leurent, Martin & Da Costa, Pascal & Jasserand, Frédéric & Rämä, Miika & Persson, Urban, 2018. "Cost and climate savings through nuclear district heating in a French urban area," Energy Policy, Elsevier, vol. 115(C), pages 616-630.
    13. Zhou, Xiao & Huang, Lu & Porter, Alan & Vicente-Gomila, Jose M., 2019. "Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 785-794.
    14. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    15. Dirk Meissner & Maxim Kotsemir, 2016. "Conceptualizing the innovation process towards the ‘active innovation paradigm’—trends and outlook," Journal of Innovation and Entrepreneurship, Springer, vol. 5(1), pages 1-18, December.
    16. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    17. Huang, Ying & Porter, Alan L. & Zhang, Yi & Lian, Xiangpeng & Guo, Ying, 2019. "An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 831-843.
    18. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

  3. LU, Jie & Tang, Zhong & Lin, Yujie & Zhu, Xinkai & Liu, Wenyong, 2014. "Has China’s Domestic Food Price Become More Stable? An Investigation Based on a Structural Break Regime Switching Model," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170635, Agricultural and Applied Economics Association.

    Cited by:

    1. Hovhannisyan, Vardges & Shanoyan, Aleksan, 2018. "An Empirical Analysis Of Welfare Consequences Of Rising Food Prices In Urban China: The Easi Approach," 2018 Annual Meeting, August 5-7, Washington, D.C. 273987, Agricultural and Applied Economics Association.
    2. Vardges Hovhannisyan & Marin Bozic, 2017. "Price Endogeneity and Food Demand in Urban China," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 386-406, June.
    3. Mendis, Sachintha & Hovhannisyan, Vardges, 2017. "Assessing Provincial-Level Demand For Food Quantity And Quality In China: An Easi Demand System Approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252797, Southern Agricultural Economics Association.
    4. Vardges Hovhannisyan & Aleksan Shanoyan, 2020. "An Empirical Analysis of the Welfare Consequences of Rising Food Prices in Urban China: The Easi Approach," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(4), pages 796-814, December.

  4. Huimin Chung & Jie Lu & Bruce Mizrach, 2009. "An Empirical Analysis of the Shanghai and Shenzen Limit Order Books," CQE Working Papers 0109, Center for Quantitative Economics (CQE), University of Muenster.

    Cited by:

    1. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    2. Westerlund, Joakim & Narayan, Paresh Kumar & Zheng, Xinwei, 2015. "Testing for stock return predictability in a large Chinese panel," Emerging Markets Review, Elsevier, vol. 24(C), pages 81-100.

Articles

  1. Wang, Zhiyuan & Qian, Zhongdong & Lu, Jie & Wu, Pengfei, 2019. "Effects of flow rate and rotational speed on pressure fluctuations in a double-suction centrifugal pump," Energy, Elsevier, vol. 170(C), pages 212-227.

    Cited by:

    1. Ni, Dan & Zhang, Ning & Gao, Bo & Li, Zhong & Yang, Minguan, 2020. "Dynamic measurements on unsteady pressure pulsations and flow distributions in a nuclear reactor coolant pump," Energy, Elsevier, vol. 198(C).
    2. Dehghan, Amir Arsalan & Shojaeefard, Mohammad Hassan & Roshanaei, Maryam, 2024. "Exploring a new criterion to determine the onset of cavitation in centrifugal pumps from energy-saving standpoint; experimental and numerical investigation," Energy, Elsevier, vol. 293(C).
    3. Fernández Oro, J.M. & Barrio Perotti, R. & Galdo Vega, M. & González, J., 2023. "Effect of the radial gap size on the deterministic flow in a centrifugal pump due to impeller-tongue interactions," Energy, Elsevier, vol. 278(PA).
    4. Haoqing Jiang & Wei Dong & Peixuan Li & Haichen Zhang, 2023. "Based on Wavelet and Windowed Multi-Resolution Dynamic Mode Decomposition, Transient Axial Force Analysis of a Centrifugal Pump under Variable Operating Conditions," Energies, MDPI, vol. 16(20), pages 1-25, October.
    5. Pei, Yingju & Liu, Qingyou & Wang, Chuan & Wang, Guorong, 2021. "Energy efficiency prediction model and energy characteristics of subsea disc pump based on velocity slip and similarity theory," Energy, Elsevier, vol. 229(C).
    6. Jiao, Weixuan & Chen, Hongjun & Cheng, Li & Zhang, Bowen & Gu, Yangdong, 2023. "Energy loss and pressure fluctuation characteristics of coastal two-way channel pumping stations under the ultra-low head condition," Energy, Elsevier, vol. 278(PA).
    7. Chengshuo Wu & Jun Yang & Shuai Yang & Peng Wu & Bin Huang & Dazhuan Wu, 2023. "A Review of Fluid-Induced Excitations in Centrifugal Pumps," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    8. Zhang, Ning & Jiang, Junxian & Gao, Bo & Liu, Xiaokai, 2020. "DDES analysis of unsteady flow evolution and pressure pulsation at off-design condition of a centrifugal pump," Renewable Energy, Elsevier, vol. 153(C), pages 193-204.
    9. Faye Jin & Zhifeng Yao & Duanming Li & Ruofu Xiao & Fujun Wang & Chenglian He, 2019. "Experimental Investigation of Transient Characteristics of a Double Suction Centrifugal Pump System during Starting Period," Energies, MDPI, vol. 12(21), pages 1-28, October.
    10. Li, Xiaojun & Chen, Hui & Chen, Bo & Luo, Xianwu & Yang, Baofeng & Zhu, Zuchao, 2020. "Investigation of flow pattern and hydraulic performance of a centrifugal pump impeller through the PIV method," Renewable Energy, Elsevier, vol. 162(C), pages 561-574.
    11. Sonawat, Arihant & Kim, Sung & Ma, Sang-Bum & Kim, Seung-Jun & Lee, Ju Beak & Yu, Myo Suk & Kim, Jin-Hyuk, 2022. "Investigation of unsteady pressure fluctuations and methods for its suppression for a double suction centrifugal pump," Energy, Elsevier, vol. 252(C).

  2. Lu, Jie & Bai, Zhaohai & Velthof, Gerard L. & Wu, Zhiguo & Chadwick, David & Ma, Lin, 2019. "Accumulation and leaching of nitrate in soils in wheat-maize production in China," Agricultural Water Management, Elsevier, vol. 212(C), pages 407-415.

    Cited by:

    1. Li, Yue & Xu, Xu & Hu, Min & Chen, Zhijun & Tan, Junwei & Liu, Liu & Xiong, Yunwu & Huang, Quanzhong & Huang, Guanhua, 2023. "Modeling water−salt−nitrogen dynamics and crop growth of saline maize farmland in Northwest China: Searching for appropriate irrigation and N fertilization strategies," Agricultural Water Management, Elsevier, vol. 282(C).
    2. Li, Yue & Huang, Guanhua & Chen, Zhijun & Xiong, Yuwu & Huang, Quanzhong & Xu, Xu & Huo, Zailin, 2022. "Effects of irrigation and fertilization on grain yield, water and nitrogen dynamics and their use efficiency of spring wheat farmland in an arid agricultural watershed of Northwest China," Agricultural Water Management, Elsevier, vol. 260(C).
    3. Yang, Wenjie & Li, Yanhang & Jia, Bingli & Liu, Lei & Yuan, Aijing & Liu, Jinshan & Qiu, Weihong, 2024. "Optimized fertilization based on fallow season precipitation and the Nutrient Expert system for dryland wheat reduced environmental risks and increased economic benefits," Agricultural Water Management, Elsevier, vol. 291(C).
    4. Jia, Dianyong & Dai, Xinglong & Xie, Yuli & He, Mingrong, 2021. "Alternate furrow irrigation improves grain yield and nitrogen use efficiency in winter wheat," Agricultural Water Management, Elsevier, vol. 244(C).
    5. Shuangshuang Xiao & Xiajiao Liu & Wei Zhang & Yingying Ye & Wurong Chen & Kelin Wang, 2022. "Tillage-Induced Fragmentation of Large Soil Macroaggregates Increases Nitrogen Leaching in a Subtropical Karst Region," Land, MDPI, vol. 11(10), pages 1-13, September.
    6. Li, Haoran & Wang, Hongguang & Fang, Qin & Jia, Bin & Li, Dongxiao & He, Jianning & Li, Ruiqi, 2023. "Effects of irrigation and nitrogen application on NO3--N distribution in soil, nitrogen absorption, utilization and translocation by winter wheat," Agricultural Water Management, Elsevier, vol. 276(C).
    7. Zhao, Chenhao & Zhang, Lina & Zhang, Qiang & Wang, Jun & Wang, Shengsen & Zhang, Min & Liu, Zhiguang, 2022. "The effects of bio-based superabsorbent polymers on the water/nutrient retention characteristics and agricultural productivity of a saline soil from the Yellow River Basin, China," Agricultural Water Management, Elsevier, vol. 261(C).
    8. Shen, Hongzheng & Gao, Yunhe & Sun, Kexin & Gu, Yuhui & Ma, Xiaoyi, 2023. "Effects of differential irrigation and nitrogen reduction replacement on winter wheat yield and water productivity and nitrogen-use efficiency," Agricultural Water Management, Elsevier, vol. 282(C).
    9. Liu, Fei & Zhu, Qing & Zhou, Zhiwen & Liao, Kaihua & Lai, Xiaoming, 2022. "Soil nitrate leaching of tea plantation and its responses to seasonal drought and wetness scenarios," Agricultural Water Management, Elsevier, vol. 260(C).
    10. Yan, Fulai & Zhang, Fucang & Fan, Xingke & Fan, Junliang & Wang, Ying & Zou, Haiyang & Wang, Haidong & Li, Guodong, 2021. "Determining irrigation amount and fertilization rate to simultaneously optimize grain yield, grain nitrogen accumulation and economic benefit of drip-fertigated spring maize in northwest China," Agricultural Water Management, Elsevier, vol. 243(C).
    11. Lu, Junsheng & Xiang, Youzhen & Fan, Junliang & Zhang, Fucang & Hu, Tiantian, 2021. "Sustainable high grain yield, nitrogen use efficiency and water productivity can be achieved in wheat-maize rotation system by changing irrigation and fertilization strategy," Agricultural Water Management, Elsevier, vol. 258(C).
    12. Xiulu Sun & Yizan Li & Marius Heinen & Henk Ritzema & Petra Hellegers & Jos van Dam, 2022. "Fertigation Strategies to Improve Water and Nitrogen Use Efficiency in Surface Irrigation System in the North China Plain," Agriculture, MDPI, vol. 13(1), pages 1-23, December.
    13. Zhang, Junwei & Xiang, Lingxiao & Liu, Yuxin & Jing, Dan & Zhang, Lili & Liu, Yong & Li, Wuqiang & Wang, Xiaoyan & Li, Tianlai & Li, Jianming, 2024. "Optimizing irrigation schedules of greenhouse tomato based on a comprehensive evaluation model," Agricultural Water Management, Elsevier, vol. 295(C).
    14. Du, Huiying & Gao, Wenxuan & Li, Jiajia & Shen, Shizhou & Wang, Feng & Fu, Li & Zhang, Keqiang, 2019. "Effects of digested biogas slurry applicationmixed with irrigation water on nitrate leaching during wheat-maize rotation in the North China Plain," Agricultural Water Management, Elsevier, vol. 213(C), pages 882-893.

  3. Martinez, J.J. & Deng, Z.D. & Titzler, P.S. & Duncan, J.P. & Lu, J. & Mueller, R.P. & Tian, C. & Trumbo, B.A. & Ahmann, M.L. & Renholds, J.F., 2019. "Hydraulic and biological characterization of a large Kaplan turbine," Renewable Energy, Elsevier, vol. 131(C), pages 240-249.

    Cited by:

    1. Zaher Mundher Yaseen & Ameen Mohammed Salih Ameen & Mohammed Suleman Aldlemy & Mumtaz Ali & Haitham Abdulmohsin Afan & Senlin Zhu & Ahmed Mohammed Sami Al-Janabi & Nadhir Al-Ansari & Tiyasha Tiyasha &, 2020. "State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations," Sustainability, MDPI, vol. 12(4), pages 1-40, February.
    2. Linda Vikström & Kjell Leonardsson & Johan Leander & Samuel Shry & Olle Calles & Gustav Hellström, 2020. "Validation of Francis–Kaplan Turbine Blade Strike Models for Adult and Juvenile Atlantic Salmon (Salmo Salar, L.) and Anadromous Brown Trout (Salmo Trutta, L.) Passing High Head Turbines," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    3. Singh, Rajesh K. & Romero-Gomez, Pedro & Colotelo, Alison H. & Perkins, William A. & Richmond, Marshall C., 2022. "Computational studies of hydraulic stressors for biological performance assessment in a hydropower plant with Kaplan turbine," Renewable Energy, Elsevier, vol. 199(C), pages 768-781.
    4. Phoevos (Foivos) Koukouvinis & John Anagnostopoulos, 2023. "State of the Art in Designing Fish-Friendly Turbines: Concepts and Performance Indicators," Energies, MDPI, vol. 16(6), pages 1-25, March.
    5. Emanuele Quaranta & Manuel Bonjean & Damiano Cuvato & Christophe Nicolet & Matthieu Dreyer & Anthony Gaspoz & Samuel Rey-Mermet & Bruno Boulicaut & Luigi Pratalata & Marco Pinelli & Giuseppe Tomaselli, 2020. "Hydropower Case Study Collection: Innovative Low Head and Ecologically Improved Turbines, Hydropower in Existing Infrastructures, Hydropeaking Reduction, Digitalization and Governing Systems," Sustainability, MDPI, vol. 12(21), pages 1-78, October.

  4. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.

    Cited by:

    1. Kiani Mavi, Reza & Kiani Mavi, Neda, 2021. "National eco-innovation analysis with big data: A common-weights model for dynamic DEA," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Joash Mageto, 2022. "Current and Future Trends of Information Technology and Sustainability in Logistics Outsourcing," Sustainability, MDPI, vol. 14(13), pages 1-27, June.
    3. Chen, Pengyu & Chu, Zhongzhu & Zhao, Miao, 2024. "The Road to corporate sustainability: The importance of artificial intelligence," Technology in Society, Elsevier, vol. 76(C).
    4. Xiaozan Lyu & Rodrigo Costas, 2020. "How do academic topics shift across altmetric sources? A case study of the research area of Big Data," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 909-943, May.
    5. Fernando Garrigós-Simón & Silvia Sanz-Blas & Yeamduan Narangajavana & Daniela Buzova, 2021. "The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    6. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    7. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    8. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    9. Douglas Mitieka & Rose Luke & Hossana Twinomurinzi & Joash Mageto, 2023. "Smart Mobility in Urban Areas: A Bibliometric Review and Research Agenda," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    10. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    11. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    12. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    13. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    14. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    15. Dhar, Suparna & Tarafdar, Pratik & Bose, Indranil, 2022. "Understanding the evolution of an emerging technological paradigm and its impact: The case of Digital Twin," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    16. Modgil, Sachin & Gupta, Shivam & Sivarajah, Uthayasankar & Bhushan, Bharat, 2021. "Big data-enabled large-scale group decision making for circular economy: An emerging market context," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    17. Majdouline, Ilias & Baz, Jamal El & Jebli, Fedwa, 2022. "Revisiting technological entrepreneurship research: An updated bibliometric analysis of the state of art," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    18. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    19. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

  5. Lu, Jie & Gupte, Akshay & Huang, Yongxi, 2018. "A mean-risk mixed integer nonlinear program for transportation network protection," European Journal of Operational Research, Elsevier, vol. 265(1), pages 277-289.

    Cited by:

    1. Li, Xin & Pan, Yanchun & Jiang, Shiqiang & Huang, Qiang & Chen, Zhimin & Zhang, Mingxia & Zhang, Zuoyao, 2021. "Locate vaccination stations considering travel distance, operational cost, and work schedule," Omega, Elsevier, vol. 101(C).
    2. Wang, Lei & Liu, Qing & Dong, Shiyu & Guedes Soares, C., 2022. "Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Andrzej Karbowski, 2021. "Generalized Benders Decomposition Method to Solve Big Mixed-Integer Nonlinear Optimization Problems with Convex Objective and Constraints Functions," Energies, MDPI, vol. 14(20), pages 1-18, October.
    4. Canbilen Sütiçen, Tuğçe & Batun, Sakine & Çelik, Melih, 2023. "Integrated reinforcement and repair of interdependent infrastructure networks under disaster-related uncertainties," European Journal of Operational Research, Elsevier, vol. 308(1), pages 369-384.
    5. Weimei Li & Leifu Gao, 2024. "Research on Risk-Averse Procurement Optimization of Emergency Supplies for Mine Thermodynamic Disasters," Mathematics, MDPI, vol. 12(14), pages 1-17, July.
    6. Chen, Zexing & Zhang, Yongjun & Tang, Wenhu & Lin, Xiaoming & Li, Qifeng, 2019. "Generic modelling and optimal day-ahead dispatch of micro-energy system considering the price-based integrated demand response," Energy, Elsevier, vol. 176(C), pages 171-183.
    7. Bei, Xiaoqiang & Zhu, Xiaoyan & Coit, David W., 2019. "A risk-averse stochastic program for integrated system design and preventive maintenance planning," European Journal of Operational Research, Elsevier, vol. 276(2), pages 536-548.
    8. Oliveira, Beatriz B. & Carravilla, Maria Antónia & Oliveira, José F. & Costa, Alysson M., 2019. "A co-evolutionary matheuristic for the car rental capacity-pricing stochastic problem," European Journal of Operational Research, Elsevier, vol. 276(2), pages 637-655.
    9. Zhong, Shaopeng & Cheng, Rong & Jiang, Yu & Wang, Zhong & Larsen, Allan & Nielsen, Otto Anker, 2020. "Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).

  6. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.

    Cited by:

    1. Xiaoyu Liu & Xuefeng Wang & Donghua Zhu, 2022. "Reviewer recommendation method for scientific research proposals: a case for NSFC," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3343-3366, June.
    2. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    3. Jiang, Hongxun & Fan, Shaokun & Zhang, Nan & Zhu, Bin, 2023. "Deep learning for predicting patent application outcome: The fusion of text and network embeddings," Journal of Informetrics, Elsevier, vol. 17(2).
    4. Hu, Ya-Han & Tai, Chun-Tien & Liu, Kang Ernest & Cai, Cheng-Fang, 2020. "Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity," Journal of Informetrics, Elsevier, vol. 14(1).
    5. Chen, Hongshu & Jin, Qianqian & Wang, Ximeng & Xiong, Fei, 2022. "Profiling academic-industrial collaborations in bibliometric-enhanced topic networks: A case study on digitalization research," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Lorena Cadavid & Kathleen Salazar-Serna, 2021. "Mapping the Research Landscape for the Motorcycle Market Policies: Sustainability as a Trend—A Systematic Literature Review," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    7. Lopreite, Milena & Misuraca, Michelangelo & Puliga, Michelangelo, 2023. "An analysis of the thematic evolution of ageing and healthcare expenditure using word embedding: A scoping review of policy implications," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    8. Hongshu Chen & Xinna Song & Qianqian Jin & Ximeng Wang, 2022. "Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6637-6660, November.
    9. Wu, Mengjia & Zhang, Yi & Zhang, Guangquan & Lu, Jie, 2021. "Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    10. Paul Donner, 2021. "Validation of the Astro dataset clustering solutions with external data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1619-1645, February.
    11. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    12. Yi Zhang & Xiaojing Cai & Caroline V. Fry & Mengjia Wu & Caroline S. Wagner, 2021. "Topic evolution, disruption and resilience in early COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4225-4253, May.
    13. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    14. Xiaowen Xi & Jiaqi Wei & Ying Guo & Weiyu Duan, 2022. "Academic collaborations: a recommender framework spanning research interests and network topology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6787-6808, November.
    15. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
    16. Woo, Seokkyun & Youtie, Jan & Ott, Ingrid & Scheu, Fenja, 2021. "Understanding the long-term emergence of autonomous vehicles technologies," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    17. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    18. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    19. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    20. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.
    21. Ioan Batrancea & Mehmet Ali Balcı & Larissa M. Batrancea & Ömer Akgüller & Horia Tulai & Mircea-Iosif Rus & Ema Speranta Masca & Ioan Dan Morar, 2024. "Topic Analysis of Social Media Posts during the COVID-19 Pandemic: Evidence from Tweets in Turkish," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 12361-12391, September.
    22. Byungun Yoon & Songhee Kim & Sunhye Kim & Hyeonju Seol, 2022. "Doc2vec-based link prediction approach using SAO structures: application to patent network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5385-5414, September.
    23. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    24. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    25. Benjamin M. Knisely & Holly H. Pavliscsak, 2023. "Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3197-3224, May.
    26. Weibin Lin & Xianli Wu & Zhengwei Wang & Xiaoji Wan & Hailin Li, 2022. "Topic Network Analysis Based on Co-Occurrence Time Series Clustering," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
    27. Gao, Xue & Zhang, Yi, 2022. "What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    28. Chowdhury, K.P., 2021. "Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers," Journal of Informetrics, Elsevier, vol. 15(1).
    29. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    30. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    31. Xie, Qing & Zhang, Xinyuan & Song, Min, 2021. "A network embedding-based scholar assessment indicator considering four facets: Research topic, author credit allocation, field-normalized journal impact, and published time," Journal of Informetrics, Elsevier, vol. 15(4).
    32. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.

  7. Shpak, Max & Ni, Yang & Lu, Jie & Müller, Peter, 2017. "Variance in estimated pairwise genetic distance under high versus low coverage sequencing: The contribution of linkage disequilibrium," Theoretical Population Biology, Elsevier, vol. 117(C), pages 51-63.

    Cited by:

    1. Freitas, Osmar & Araujo, Sabrina B.L. & Campos, Paulo R.A., 2022. "Speciation in a metapopulation model upon environmental changes," Ecological Modelling, Elsevier, vol. 468(C).

  8. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.

    Cited by:

    1. Wu, Mengjia & Zhang, Yi & Zhang, Guangquan & Lu, Jie, 2021. "Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    2. Hui Guang & Ying Liu & Jiao Feng & Nan Wang, 2024. "Smart Manufacturing and Enterprise Breakthrough Innovation: Co-Existence Test of “U-Shaped” and Inverted “U-Shaped” Relationships in Chinese Listed Companies," Sustainability, MDPI, vol. 16(14), pages 1-19, July.
    3. Hoyoon Lee & Dawoon Jeong & Jeong-Dong Lee, 2023. "Drivers of institutional evolution: phylogenetic inertia and ecological pressure," Journal of Evolutionary Economics, Springer, vol. 33(2), pages 279-308, April.
    4. Juhyun Lee & Sangsung Park & Junseok Lee, 2023. "Exploring Potential R&D Collaboration Partners Using Embedding of Patent Graph," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    5. Mariani, Manuel Sebastian & Medo, Matúš & Lafond, François, 2019. "Early identification of important patents: Design and validation of citation network metrics," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 644-654.
    6. Francesco Paolo Appio & Luigi de Luca & Robert Morgan & Antonella Martini, 2019. "Patent portfolio diversity and firm profitability: A question of specialization or diversification?," Post-Print halshs-02292360, HAL.
    7. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    8. Gao, Xue & Zhang, Yi, 2022. "What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    9. Zhang, Lili & Guo, Ying & Sun, Ganlu, 2019. "How patent signals affect venture capital: The evidence of bio-pharmaceutical start-ups in China," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 93-104.
    10. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    11. Jaehyuk Park, 2024. "Analyzing the direct role of governmental organizations in artificial intelligence innovation," The Journal of Technology Transfer, Springer, vol. 49(2), pages 437-465, April.
    12. Manajit Chakraborty & Maksym Byshkin & Fabio Crestani, 2020. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-28, December.
    13. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    14. Lin Zhu & Donghua Zhu & Xuefeng Wang & Scott W. Cunningham & Zhinan Wang, 2019. "An integrated solution for detecting rising technology stars in co-inventor networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 137-172, October.

  9. Amin, U. & Hossain, M.J. & Lu, J. & Fernandez, E., 2017. "Performance analysis of an experimental smart building: Expectations and outcomes," Energy, Elsevier, vol. 135(C), pages 740-753.

    Cited by:

    1. Mohammad Esmaeil Honarmand & Vahid Hosseinnezhad & Barry Hayes & Pierluigi Siano, 2021. "Local Energy Trading in Future Distribution Systems," Energies, MDPI, vol. 14(11), pages 1-19, May.
    2. Neeraj Gupta & B Rajanarayan Prusty & Omar Alrumayh & Abdulaziz Almutairi & Talal Alharbi, 2022. "The Role of Transactive Energy in the Future Energy Industry: A Critical Review," Energies, MDPI, vol. 15(21), pages 1-24, October.
    3. Bellos, Evangelos & Tzivanidis, Christos, 2018. "Multi-objective optimization of a solar driven trigeneration system," Energy, Elsevier, vol. 149(C), pages 47-62.

  10. Chen, Hongshu & Zhang, Guangquan & Zhu, Donghua & Lu, Jie, 2017. "Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 39-52.

    Cited by:

    1. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    2. Wang, Xuefeng & Zhang, Shuo & Liu, Yuqin & Du, Jian & Huang, Heng, 2021. "How pharmaceutical innovation evolves: The path from science to technological development to marketable drugs," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    3. Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2020. "Robots and the origin of their labour-saving impact," LEM Papers Series 2020/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    5. Savin, Ivan & Ott, Ingrid & Konop, Chris, 2022. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Lee, Jiho & Ko, Namuk & Yoon, Janghyeok & Son, Changho, 2021. "An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    7. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    8. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    9. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    10. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    11. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    12. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    13. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    14. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    15. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    16. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    17. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.
    18. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    19. Graziela Salvan Cerveira & Jorge Lima de Magalhães & Adelaide Maria de Souza Antunes, 2022. "Status and Trends of Membrane Technology for Wastewater Treatment: A Patent Analysis," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    20. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    21. Donghyun Choi & Bomi Song, 2018. "Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis," Sustainability, MDPI, vol. 10(8), pages 1-26, August.
    22. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    23. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

  11. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.

    Cited by:

    1. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    2. Teng, Hao & Wang, Nan & Zhao, Hongyu & Hu, Yingtong & Jin, Haitao, 2024. "Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents," Journal of Informetrics, Elsevier, vol. 18(1).
    3. Gaizka Garechana & Rosa Río-Belver & Enara Zarrabeitia & Izaskun Alvarez-Meaza, 2022. "TeknoAssistant : a domain specific tech mining approach for technical problem-solving support," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5459-5473, September.
    4. He, Xi-jun & Meng, Xue & Dong, Yan-bo & Wu, Yu-ying, 2019. "Demand identification model of potential technology based on SAO structure semantic analysis: The case of new energy and energy saving fields," Technology in Society, Elsevier, vol. 58(C).
    5. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    6. Xu, Jianguo & Guo, Lixiang & Jiang, Jiang & Ge, Bingfeng & Li, Mengjun, 2019. "A deep learning methodology for automatic extraction and discovery of technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 339-351.
    7. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    8. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.

  12. Yi Zhang & Guangquan Zhang & Donghua Zhu & Jie Lu, 2017. "Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1925-1939, August.

    Cited by:

    1. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    2. María Pinto & Rosaura Fernández-Pascual & David Caballero-Mariscal & Dora Sales, 2020. "Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1479-1510, August.
    3. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    4. Yi Zhang & Xiaojing Cai & Caroline V. Fry & Mengjia Wu & Caroline S. Wagner, 2021. "Topic evolution, disruption and resilience in early COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4225-4253, May.
    5. Kajikawa, Yuya & Mejia, Cristian & Wu, Mengjia & Zhang, Yi, 2022. "Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    7. Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    8. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    9. Leonid Gokhberg & Dirk Meissner & Ilya Kuzminov, 2023. "What semantic analysis can tell us about long term trends in the global STI policy agenda," The Journal of Technology Transfer, Springer, vol. 48(6), pages 2249-2277, December.
    10. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    11. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    12. Alexey Lyutov & Yilmaz Uygun & Marc-Thorsten Hütt, 2021. "Machine learning misclassification of academic publications reveals non-trivial interdependencies of scientific disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1173-1186, February.
    13. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).
    14. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    15. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    16. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    17. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
    18. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    19. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.

  13. Zhang, Yi & Robinson, Douglas K.R. & Porter, Alan L. & Zhu, Donghua & Zhang, Guangquan & Lu, Jie, 2016. "Technology roadmapping for competitive technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 175-186.
    See citations under working paper version above.
  14. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.

    Cited by:

    1. Tingcan Ma & Ruinan Li & Guiyan Ou & Mingliang Yue, 2018. "Topic based research competitiveness evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 789-803, November.
    2. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    3. Minhao Xiang & Dian Fu & Kun Lv, 2023. "Identifying and Predicting Trends of Disruptive Technologies: An Empirical Study Based on Text Mining and Time Series Forecasting," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    4. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    5. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    6. Jun, Seung-Pyo & Lee, Jae-Seong & Lee, Juyeon, 2020. "Method of improving the performance of public-private innovation networks by linking heterogeneous DBs: Prediction using ensemble and PPDM models," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    7. Chien-Wei Chuang & Ariana Chang & Mingchih Chen & Maria John P. Selvamani & Ben-Chang Shia, 2022. "A Worldwide Bibliometric Analysis of Publications on Artificial Intelligence and Ethics in the Past Seven Decades," Sustainability, MDPI, vol. 14(18), pages 1-13, September.
    8. Hyejin Park & Han Woo Park, 2018. "Two-side face of knowledge building using scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(6), pages 2815-2836, November.
    9. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    10. Pavel Bakhtin & Ozcan Saritas & Alexander Chulok & Ilya Kuzminov & Anton Timofeev, 2017. "Trend monitoring for linking science and strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2059-2075, June.
    11. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    12. Wu, Mengjia & Zhang, Yi & Zhang, Guangquan & Lu, Jie, 2021. "Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    13. Jongchan Kim & Joonhyuck Lee & Gabjo Kim & Sangsung Park & Dongsik Jang, 2016. "A Hybrid Method of Analyzing Patents for Sustainable Technology Management in Humanoid Robot Industry," Sustainability, MDPI, vol. 8(5), pages 1-14, May.
    14. Jeon, Daeseong & Lee, Junyoup & Ahn, Joon Mo & Lee, Changyong, 2023. "Measuring the novelty of scientific publications: A fastText and local outlier factor approach," Journal of Informetrics, Elsevier, vol. 17(4).
    15. Boubaker, Sabri & Liu, Zhenya & Zhai, Ling, 2021. "Big data, news diversity and financial market crash," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    16. Huang, Ying & Porter, Alan L. & Cunningham, Scott W. & Robinson, Douglas K.R. & Liu, Jianhua & Zhu, Donghua, 2018. "A technology delivery system for characterizing the supply side of technology emergence: Illustrated for Big Data & Analytics," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 165-176.
    17. Yuxue Yang & Xuejiao Tan & Yafei Shi & Jun Deng, 2023. "What are the core concerns of policy analysis? A multidisciplinary investigation based on in-depth bibliometric analysis," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    18. Yi Zhang & Xiaojing Cai & Caroline V. Fry & Mengjia Wu & Caroline S. Wagner, 2021. "Topic evolution, disruption and resilience in early COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4225-4253, May.
    19. Hei-Chia Wang & Tzu-Ting Hsu & Yunita Sari, 2019. "Personal research idea recommendation using research trends and a hierarchical topic model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1385-1406, December.
    20. Russo, Margherita & Pavone, Pasquale, 2021. "Evidence-based portfolios of innovation policy mixes: A cross-country analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    21. Azimi, Sasan & Rahmani, Rouhollah & Fateh-rad, Mahdi, 2019. "Investment cost optimization for industrial project portfolios using technology mining," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 243-253.
    22. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    23. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    24. Saba Sareminia & Alireza Hasanzadeh & Shaaban Elahi & Gholamali Montazer, 2019. "Developing Technology Roadmapping Combinational Framework by Meta Synthesis Technique," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 1-36, April.
    25. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    26. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    27. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    28. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    29. Yu, Qinyao, 2022. "Simulation of the interactive prediction of contemporary social change and religious socialization based on big data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    30. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Liu, Ziqiang & Yuan, Guoting, 2020. "Topic-linked innovation paths in science and technology," Journal of Informetrics, Elsevier, vol. 14(2).
    31. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, October.
    32. Mohammed Azmi Al-Betar & Ammar Kamal Abasi & Ghazi Al-Naymat & Kamran Arshad & Sharif Naser Makhadmeh, 2023. "Optimization of scientific publications clustering with ensemble approach for topic extraction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2819-2877, May.
    33. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    34. Ilya Kuzminov & Pavel Bakhtin & Elena Khabirova & Maxim Kotsemir & Alina Lavrynenko, 2018. "Mapping the Radical Innovations in Food Industry: A Text Mining Study," HSE Working papers WP BRP 80/STI/2018, National Research University Higher School of Economics.
    35. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    36. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    37. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    38. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    39. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    40. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    41. Jian Mou & Gang Ren & Chunxiu Qin & Kerry Kurcz, 2019. "Understanding the topics of export cross-border e-commerce consumers feedback: an LDA approach," Electronic Commerce Research, Springer, vol. 19(4), pages 749-777, December.
    42. Fu, Zhongmeng & Cao, Yuan & Zhao, Yong, 2024. "Identifying knowledge evolution in computer science from the perspective of academic genealogy," Journal of Informetrics, Elsevier, vol. 18(2).
    43. Berg, S. & Wustmans, M. & Bröring, S., 2019. "Identifying first signals of emerging dominance in a technological innovation system: A novel approach based on patents," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 706-722.
    44. Yi Bu & Mengyang Li & Weiye Gu & Win‐bin Huang, 2021. "Topic diversity: A discipline scheme‐free diversity measurement for journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 523-539, May.

  15. Qian, Zhongdong & Wang, Fan & Guo, Zhiwei & Lu, Jie, 2016. "Performance evaluation of an axial-flow pump with adjustable guide vanes in turbine mode," Renewable Energy, Elsevier, vol. 99(C), pages 1146-1152.

    Cited by:

    1. Liu, Yabin & Tan, Lei, 2018. "Tip clearance on pressure fluctuation intensity and vortex characteristic of a mixed flow pump as turbine at pump mode," Renewable Energy, Elsevier, vol. 129(PA), pages 606-615.
    2. Yang, Fan & Li, Zhongbin & Yuan, Yao & Lin, Zhikang & Zhou, Guangxin & Ji, Qingwei, 2022. "Study on vortex flow and pressure fluctuation in dustpan-shaped conduit of a low head axial-flow pump as turbine," Renewable Energy, Elsevier, vol. 196(C), pages 856-869.
    3. Binama, Maxime & Su, Wen-Tao & Li, Xiao-Bin & Li, Feng-Chen & Wei, Xian-Zhu & An, Shi, 2017. "Investigation on pump as turbine (PAT) technical aspects for micro hydropower schemes: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 148-179.
    4. Wang, Tao & Wang, Chuan & Kong, Fanyu & Gou, Qiuqin & Yang, Sunsheng, 2017. "Theoretical, experimental, and numerical study of special impeller used in turbine mode of centrifugal pump as turbine," Energy, Elsevier, vol. 130(C), pages 473-485.
    5. Renzi, Massimiliano & Rudolf, Pavel & Štefan, David & Nigro, Alessandra & Rossi, Mosè, 2019. "Installation of an axial Pump-as-Turbine (PaT) in a wastewater sewer of an oil refinery: A case study," Applied Energy, Elsevier, vol. 250(C), pages 665-676.
    6. Zhao, Ziwen & Yuan, Yichen & He, Mengjiao & Jurasz, Jakub & Wang, Jianan & Egusquiza, Mònica & Egusquiza, Eduard & Xu, Beibei & Chen, Diyi, 2022. "Stability and efficiency performance of pumped hydro energy storage system for higher flexibility," Renewable Energy, Elsevier, vol. 199(C), pages 1482-1494.
    7. Rossi, Mosè & Nigro, Alessandra & Renzi, Massimiliano, 2019. "Experimental and numerical assessment of a methodology for performance prediction of Pumps-as-Turbines (PaTs) operating in off-design conditions," Applied Energy, Elsevier, vol. 248(C), pages 555-566.
    8. Li, Deyou & Chang, Hong & Zuo, Zhigang & Wang, Hongjie & Li, Zhenggui & Wei, Xianzhu, 2020. "Experimental investigation of hysteresis on pump performance characteristics of a model pump-turbine with different guide vane openings," Renewable Energy, Elsevier, vol. 149(C), pages 652-663.
    9. Wang, Tao & Xiang, Ru & Yu, He & Zhou, Min, 2023. "Performance improvement of forward-curved impeller with an adequate outlet swirl using in centrifugal pump as turbine," Renewable Energy, Elsevier, vol. 204(C), pages 67-76.
    10. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    11. Shojaeefard, Mohammad Hassan & Saremian, Salman, 2023. "Studying the impact of impeller geometrical parameters on the high-efficiency working range of pump as turbine (PAT) installed in the water distribution network," Renewable Energy, Elsevier, vol. 216(C).
    12. Qi, Bing & Zhang, Desheng & Geng, Linlin & Zhao, Ruijie & van Esch, Bart P.M., 2022. "Numerical and experimental investigations on inflow loss in the energy recovery turbines with back-curved and front-curved impeller based on the entropy generation theory," Energy, Elsevier, vol. 239(PE).
    13. Li, Deyou & Song, Yechen & Lin, Song & Wang, Hongjie & Qin, Yonglin & Wei, Xianzhu, 2021. "Effect mechanism of cavitation on the hump characteristic of a pump-turbine," Renewable Energy, Elsevier, vol. 167(C), pages 369-383.
    14. Yuquan Zhang & Yanhe Xu & Yuan Zheng & E. Fernandez-Rodriguez & Aoran Sun & Chunxia Yang & Jue Wang, 2019. "Multiobjective Optimization Design and Experimental Investigation on the Axial Flow Pump with Orthogonal Test Approach," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    15. Postacchini, Matteo & Di Giuseppe, Elisa & Eusebi, Anna Laura & Pelagalli, Leonardo & Darvini, Giovanna & Cipolletta, Giulia & Fatone, Francesco, 2022. "Energy saving from small-sized urban contexts: Integrated application into the domestic water cycle," Renewable Energy, Elsevier, vol. 199(C), pages 1300-1317.
    16. Lin, Tong & Li, Xiaojun & Zhu, Zuchao & Xie, Jing & Li, Yi & Yang, Hui, 2021. "Application of enstrophy dissipation to analyze energy loss in a centrifugal pump as turbine," Renewable Energy, Elsevier, vol. 163(C), pages 41-55.
    17. Simin Shen & Zhongdong Qian & Bin Ji, 2019. "Numerical Analysis of Mechanical Energy Dissipation for an Axial-Flow Pump Based on Entropy Generation Theory," Energies, MDPI, vol. 12(21), pages 1-22, October.
    18. Liu, Ming & Tan, Lei & Cao, Shuliang, 2019. "Theoretical model of energy performance prediction and BEP determination for centrifugal pump as turbine," Energy, Elsevier, vol. 172(C), pages 712-732.
    19. Ghorani, Mohammad Mahdi & Sotoude Haghighi, Mohammad Hadi & Maleki, Ali & Riasi, Alireza, 2020. "A numerical study on mechanisms of energy dissipation in a pump as turbine (PAT) using entropy generation theory," Renewable Energy, Elsevier, vol. 162(C), pages 1036-1053.
    20. Morabito, Alessandro & Vagnoni, Elena & Di Matteo, Mariano & Hendrick, Patrick, 2021. "Numerical investigation on the volute cutwater for pumps running in turbine mode," Renewable Energy, Elsevier, vol. 175(C), pages 807-824.
    21. Jin, Yongxin & Zhang, Desheng & Song, Wenwu & Shen, Xi & Shi, Lei & Lu, Jiaxing, 2022. "Numerical study on energy conversion characteristics of molten salt pump based on energy transport theory," Energy, Elsevier, vol. 244(PA).
    22. Wang, Tao & Kong, Fanyu & Xia, Bin & Bai, Yuxing & Wang, Chuan, 2017. "The method for determining blade inlet angle of special impeller using in turbine mode of centrifugal pump as turbine," Renewable Energy, Elsevier, vol. 109(C), pages 518-528.
    23. Daqing Zhou & Huixiang Chen & Yuan Zheng & Kan Kan & An Yu & Maxime Binama, 2019. "Development and Numerical Performance Analysis of a Pump Directly Driven by a Hydrokinetic Turbine," Energies, MDPI, vol. 12(22), pages 1-20, November.

  16. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.

    Cited by:

    1. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    3. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Jiang, Hongxun & Fan, Shaokun & Zhang, Nan & Zhu, Bin, 2023. "Deep learning for predicting patent application outcome: The fusion of text and network embeddings," Journal of Informetrics, Elsevier, vol. 17(2).
    5. Moehrle, Martin G. & Frischkorn, Jonas, 2021. "Bridge strongly or focus – An analysis of bridging patents in four application fields of carbon fiber reinforcements," Journal of Informetrics, Elsevier, vol. 15(2).
    6. Eungchan Kim & Young Seok Ock & Seung-Jun Shin & Wonchul Seo, 2018. "An Approach to Generating Reference Information for Technology Evaluation," Sustainability, MDPI, vol. 10(9), pages 1-19, September.
    7. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    8. An, Xin & Li, Jinghong & Xu, Shuo & Chen, Liang & Sun, Wei, 2021. "An improved patent similarity measurement based on entities and semantic relations," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    10. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    11. Munan Li, 2018. "Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 77-100, July.
    12. Zhang, Lili & Guo, Ying & Sun, Ganlu, 2019. "How patent signals affect venture capital: The evidence of bio-pharmaceutical start-ups in China," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 93-104.
    13. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.
    14. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    15. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).

  17. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2016. "A safety-critical decision support system evaluation using situation awareness and workload measures," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 147-159.

    Cited by:

    1. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Che, Haiyang, 2021. "A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

  18. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2015. "An abnormal situation modeling method to assist operators in safety-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 33-47.

    Cited by:

    1. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Enliang Guo & Jiquan Zhang & Yongfang Wang & Ha Si & Feng Zhang, 2016. "Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1747-1761, September.
    3. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Che, Haiyang, 2021. "A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Zhou, Jian-Lan & Lei, Yi, 2020. "A slim integrated with empirical study and network analysis for human error assessment in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2016. "A safety-critical decision support system evaluation using situation awareness and workload measures," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 147-159.

  19. Jie Lu & John Aldrich & Tianjian Shi, 2014. "Revisiting Media Effects in Authoritarian Societies," Politics & Society, , vol. 42(2), pages 253-283, June.

    Cited by:

    1. Chun-Fang Chiang & Jason M. Kuo & Megumi Naoi & Jin-Tan Liu, 2020. "What Do Voters Learn from Foreign News? Emulation, Backlash, and Public Support for Trade Agreements," NBER Working Papers 27497, National Bureau of Economic Research, Inc.
    2. Zhenhua Su & Qian Zhou & Yanyu Ye & Dongxiao Li, 2021. "How the media construct happiness under cultural perspective in China: Through collectivistic and individualistic values," Social Science Quarterly, Southwestern Social Science Association, vol. 102(6), pages 2619-2639, November.
    3. John James Kennedy & Haruka Nagao & Hongyan Liu, 2018. "Voting and Values: Grassroots Elections in Rural and Urban China," Politics and Governance, Cogitatio Press, vol. 6(2), pages 90-102.

  20. Liang Gong & Wei Zhang & Jianding Zhou & Jie Lu & Hua Xiong & Xueli Shi & Jianqiang Chen, 2013. "Prognostic Value of HIFs Expression in Head and Neck Cancer: A Systematic Review," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-8, September.

    Cited by:

    1. Antonello Vidiri & Emma Gangemi & Emanuela Ruberto & Rosella Pasqualoni & Rosa Sciuto & Giuseppe Sanguineti & Alessia Farneti & Maria Benevolo & Francesca Rollo & Francesca Sperati & Filomena Spasiano, 2020. "Correlation between histogram-based DCE-MRI parameters and 18F-FDG PET values in oropharyngeal squamous cell carcinoma: Evaluation in primary tumors and metastatic nodes," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.

  21. Chung, Huimin & Gao, Cheng & Lu, Jie & Mizrach, Bruce, 2013. "An empirical analysis of the Shanghai and Shenzhen limit order books," Economic Modelling, Elsevier, vol. 34(C), pages 37-41.
    See citations under working paper version above.
  22. Cho, E.-S. & Shin, D. & Lu, J. & de Jong, W. & Roekaerts, D.J.E.M., 2013. "Configuration effects of natural gas fired multi-pair regenerative burners in a flameless oxidation furnace on efficiency and emissions," Applied Energy, Elsevier, vol. 107(C), pages 25-32.

    Cited by:

    1. Sorrentino, Giancarlo & Sabia, Pino & Bozza, Pio & Ragucci, Raffaele & de Joannon, Mara, 2017. "Impact of external operating parameters on the performance of a cyclonic burner with high level of internal recirculation under MILD combustion conditions," Energy, Elsevier, vol. 137(C), pages 1167-1174.
    2. Yepes, Hernando A. & Obando, Julián E. & Amell, Andrés A., 2022. "The effect of syngas addition on flameless natural gas combustion in a regenerative furnace," Energy, Elsevier, vol. 252(C).
    3. Kruse, Stephan & Kerschgens, Bruno & Berger, Lukas & Varea, Emilien & Pitsch, Heinz, 2015. "Experimental and numerical study of MILD combustion for gas turbine applications," Applied Energy, Elsevier, vol. 148(C), pages 456-465.
    4. Tsuboi, Yosuke & Ito, Shintaro & Takafuji, Makoto & Ohara, Hiroaki & Fujimori, Toshiro, 2017. "Development of a regenerative reformer for tar-free syngas production in a steam gasification process," Applied Energy, Elsevier, vol. 185(P2), pages 1217-1224.
    5. Li, Zhiyi & Ferrarotti, Marco & Cuoci, Alberto & Parente, Alessandro, 2018. "Finite-rate chemistry modelling of non-conventional combustion regimes using a Partially-Stirred Reactor closure: Combustion model formulation and implementation details," Applied Energy, Elsevier, vol. 225(C), pages 637-655.

  23. Tang, Jia & Fang, Jiang-ping & Li, Ping & Guo, Jian-bin & Lu, Jie & Yuan, Qing-juan, 2012. "The Function and Value of Water Conservation of Forest Ecosystem in Gongbo Nature Reserve of Tibet," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 4(01), pages 1-3, January.

    Cited by:

    1. Zhang, Jin & Xu, Linyu & Li, Xiaojin, 2015. "Review on the externalities of hydropower: A comparison between large and small hydropower projects in Tibet based on the CO2 equivalent," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 176-185.

  24. Xiawen Zheng & Li Wang & Yaowu Zhu & Qing Guan & Huijun Li & Zhigang Xiong & Lingyan Deng & Jie Lu & Xiaoping Miao & Liming Cheng, 2012. "The SNP rs961253 in 20p12.3 Is Associated with Colorectal Cancer Risk: A Case-Control Study and a Meta-Analysis of the Published Literature," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.

    Cited by:

    1. Yan Yun & Chi Ma & XiaoChun Ma, 2014. "The SNP rs1883832 in CD40 Gene and Risk of Atherosclerosis in Chinese Population: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-12, May.

  25. Jincheng Ding & Zheng Xia & Jie Lu, 2012. "Esterification and Deacidification of a Waste Cooking Oil (TAN 68.81 mg KOH/g) for Biodiesel Production," Energies, MDPI, vol. 5(8), pages 1-9, July.

    Cited by:

    1. Ming-Chien Hsiao & Li-Wen Chang & Shuhn-Shyurng Hou, 2019. "Study of Solid Calcium Diglyceroxide for Biodiesel Production from Waste Cooking Oil Using a High Speed Homogenizer," Energies, MDPI, vol. 12(17), pages 1-11, August.
    2. Erika Carnevale & Giovanni Molari & Matteo Vittuari, 2017. "Used Cooking Oils in the Biogas Chain: A Technical and Economic Assessment," Energies, MDPI, vol. 10(2), pages 1-13, February.
    3. Yu, Dongmin & Duan, Chuanxu & Gu, Bing, 2023. "UiO-66-NH2@MnFe2O4 as a novel and retrievable MOF nanocatalyst for biodiesel synthesis from utilized edible oil in a microwave reactor: RSM design and CI engine studies," Renewable Energy, Elsevier, vol. 219(P1).

  26. Zhang, Ruijun & Lu, Jie & Zhang, Guangquan, 2011. "A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces," European Journal of Operational Research, Elsevier, vol. 215(1), pages 194-203, November.

    Cited by:

    1. Liu, Xiong & Feng, Huijun & Chen, Lingen & Qin, Xiaoyong & Sun, Fengrui, 2016. "Hot metal yield optimization of a blast furnace based on constructal theory," Energy, Elsevier, vol. 104(C), pages 33-41.
    2. Djeumou Fomeni, Franklin, 2018. "A multi-objective optimization approach for the blending problem in the tea industry," International Journal of Production Economics, Elsevier, vol. 205(C), pages 179-192.
    3. Choicharoon, Aritad & Hodgett, Richard & Summers, Barbara & Siraj, Sajid, 2024. "Hit or miss: A decision support system framework for signing new musical talent," European Journal of Operational Research, Elsevier, vol. 312(1), pages 324-337.

  27. Angang Hu & Jie Lu & Zhengyan Xiao, 2011. "Has China's Economy Become More Stable and Inertial? Nonlinear Investigations Based on Structural Break and Duration Dependent Regime Switching Models," Annals of Economics and Finance, Society for AEF, vol. 12(1), pages 157-181, May.

    Cited by:

    1. Woon Kan Yap & Siong Hock Law & Judhiana Abdul-Ghani, 2019. "Effects of Credit Market Freedom on Output Reallocation in China's Banking Sector Through the Intermediation of Cost X-inefficiency," Annals of Economics and Finance, Society for AEF, vol. 20(2), pages 691-720, November.

  28. Ya Gao & Guangquan Zhang & Jie Lu & Hui-Ming Wee, 2011. "Particle swarm optimization for bi-level pricing problems in supply chains," Journal of Global Optimization, Springer, vol. 51(2), pages 245-254, October.

    Cited by:

    1. Grzegorz Sroka & Mariusz Oszust, 2021. "Approximation of the Constant in a Markov-Type Inequality on a Simplex Using Meta-Heuristics," Mathematics, MDPI, vol. 9(3), pages 1-10, January.
    2. Ata Allah Taleizadeh, 2017. "Stochastic Multi-Objectives Supply Chain Optimization with Forecasting Partial Backordering Rate: A Novel Hybrid Method of Meta Goal Programming and Evolutionary Algorithms," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-28, August.
    3. Qiu, Xuan & Huang, George Q., 2016. "Transportation service sharing and replenishment/delivery scheduling in Supply Hub in Industrial Park (SHIP)," International Journal of Production Economics, Elsevier, vol. 175(C), pages 109-120.
    4. Sunil Tiwari & Chandra K. Jaggi & Asoke Kumar Bhunia & Ali Akbar Shaikh & Mark Goh, 2017. "Two-warehouse inventory model for non-instantaneous deteriorating items with stock-dependent demand and inflation using particle swarm optimization," Annals of Operations Research, Springer, vol. 254(1), pages 401-423, July.
    5. Chen, Xu & Li, Ling & Zhou, Ming, 2012. "Manufacturer's pricing strategy for supply chain with warranty period-dependent demand," Omega, Elsevier, vol. 40(6), pages 807-816.
    6. Chung-Yuan Dye & Tsu-Pang Hsieh, 2013. "A particle swarm optimization for solving lot-sizing problem with fluctuating demand and preservation technology cost under trade credit," Journal of Global Optimization, Springer, vol. 55(3), pages 655-679, March.

  29. Guangquan Zhang & Jie Lu, 2010. "Fuzzy bilevel programming with multiple objectives and cooperative multiple followers," Journal of Global Optimization, Springer, vol. 47(3), pages 403-419, July.

    Cited by:

    1. Ya Gao & Guangquan Zhang & Jie Lu & Hui-Ming Wee, 2011. "Particle swarm optimization for bi-level pricing problems in supply chains," Journal of Global Optimization, Springer, vol. 51(2), pages 245-254, October.
    2. Aihong Ren & Yuping Wang, 2014. "A cutting plane method for bilevel linear programming with interval coefficients," Annals of Operations Research, Springer, vol. 223(1), pages 355-378, December.
    3. Hua Ke & Junjie Ma & Guangdong Tian, 2017. "Hybrid multilevel programming with uncertain random parameters," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 589-596, March.
    4. Adejuyigbe O. Fajemisin & Laura Climent & Steven D. Prestwich, 2021. "An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 665-692, September.
    5. Ma, Y. & Li, Y.P. & Huang, G.H. & Zhang, Y.F. & Liu, Y.R. & Wang, H. & Ding, Y.K., 2022. "Planning water-food-ecology nexus system under uncertainty: Tradeoffs and synergies in Central Asia," Agricultural Water Management, Elsevier, vol. 266(C).

  30. Ya Gao & Guangquan Zhang & Jie Lu, 2009. "A Fuzzy Multi-Objective Bilevel Decision Support System," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 93-108.

    Cited by:

    1. Konur, Dinçer & Golias, Mihalis M., 2013. "Cost-stable truck scheduling at a cross-dock facility with unknown truck arrivals: A meta-heuristic approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 71-91.
    2. Anil Gupta & Nikita Dogra, 2017. "Tourist adoption of mapping apps: a UTAUT2 perspective of smart travellers," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 23(2), pages 145-1416-1, November.

  31. Jie Lu & Chenggen Shi & Guangquan Zhang & Da Ruan, 2007. "An Extended Branch And Bound Algorithm For Bilevel Multi-Follower Decision Making In A Referential-Uncooperative Situation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 371-388.

    Cited by:

    1. Jalali, Mehdi & Zare, Kazem & Seyedi, Heresh, 2017. "Strategic decision-making of distribution network operator with multi-microgrids considering demand response program," Energy, Elsevier, vol. 141(C), pages 1059-1071.
    2. Adejuyigbe O. Fajemisin & Laura Climent & Steven D. Prestwich, 2021. "An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 665-692, September.
    3. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh, 2018. "A multi-follower bilevel stochastic programming approach for energy management of combined heat and power micro-grids," Energy, Elsevier, vol. 149(C), pages 135-146.

  32. Jie Lu, 2003. "A Model for Evaluating E-Commerce Based on Cost/Benefit and Customer Satisfaction," Information Systems Frontiers, Springer, vol. 5(3), pages 265-277, September.

    Cited by:

    1. Corlane Barclay, 2008. "Towards an integrated measurement of IS project performance: The project performance scorecard," Information Systems Frontiers, Springer, vol. 10(3), pages 331-345, July.
    2. Ong, Chorng-Shyong & Chang, Shu-Chen & Lee, Shwn-Meei, 2015. "Development of WebHapp: Factors in predicting user perceptions of website-related happiness," Journal of Business Research, Elsevier, vol. 68(3), pages 591-598.
    3. Eleonora Lorenzini, 2012. "Innovation and e-commerce in clusters of small firms: The case of a regional e-marketplace," DEM Working Papers Series 003, University of Pavia, Department of Economics and Management.
    4. Eleonora Lorenzini, 2014. "Innovation and e-commerce in clusters of small firms: The case of a regional e-marketplace," Local Economy, London South Bank University, vol. 29(8), pages 771-794, December.

  33. J Lu & H Ohta, 2003. "Digital contracts-driven method for pricing complex derivatives," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(9), pages 1002-1010, September.

    Cited by:

    1. Fei Chen & Charles Sutcliffe, 2012. "Pricing And Hedging Short Sterling Options Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 128-149, April.

  34. Guangquan Zhang & Jie Lu, 2003. "An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria," Group Decision and Negotiation, Springer, vol. 12(6), pages 501-515, November.

    Cited by:

    1. Ana X. Halabi & Jairo R. Montoya-Torres & Nelson Obregón, 2012. "A Case Study of Group Decision Method for Environmental Foresight and Water Resources Planning Using a Fuzzy Approach," Group Decision and Negotiation, Springer, vol. 21(2), pages 205-232, March.
    2. Zhou, Shenghan & Liu, Wei & Chang, Wenbing, 2016. "An improved TOPSIS with weighted hesitant vague information," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 47-53.
    3. Ahmet Aytekin & Çağlar Karamaşa, 2017. "Analyzing Financial Performance Of Insurance Companies Traded In BIST via Fuzzy Shannon's Entropy Based Fuzzy TOPSIS Methodology," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 5(1), pages 51-84, June.
    4. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring," European Journal of Operational Research, Elsevier, vol. 195(3), pages 942-959, June.
    5. Zaiwu Gong & Lihong Wang, 2017. "On Consistency Test Method of Expert Opinion in Ecological Security Assessment," IJERPH, MDPI, vol. 14(9), pages 1-18, September.
    6. Kannan Govindan & R. Sivakumar, 2016. "Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches," Annals of Operations Research, Springer, vol. 238(1), pages 243-276, March.
    7. Ron Chi-Wai Kwok & Duanning Zhou & Quan Zhang & Jian Ma, 2007. "A Fuzzy Multi-Criteria Decision Making Model for IS Student Group Project Assessment," Group Decision and Negotiation, Springer, vol. 16(1), pages 25-42, January.
    8. Tseng, Ming-Lang & Lim, Ming K. & Wu, Kuo-Jui, 2019. "Improving the benefits and costs on sustainable supply chain finance under uncertainty," International Journal of Production Economics, Elsevier, vol. 218(C), pages 308-321.
    9. Kannan Govindan & R. Sivakumar, 2016. "Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches," Annals of Operations Research, Springer, vol. 238(1), pages 243-276, March.
    10. İrfan Ertuğrul, 2011. "Fuzzy Group Decision Making for the Selection of Facility Location," Group Decision and Negotiation, Springer, vol. 20(6), pages 725-740, November.
    11. Chiu-Chi Wei & Chih-Chien Tai & Shun-Chin Lee & Meng-Ling Chang, 2023. "Assessing Knowledge Quality Using Fuzzy MCDM Model," Mathematics, MDPI, vol. 11(17), pages 1-16, August.
    12. Wen-Hsien Tsai & Ching-Chien Yang & Jun-Der Leu & Ya-Fen Lee & Chih-Hao Yang, 2013. "An Integrated Group Decision Making Support Model for Corporate Financing Decisions," Group Decision and Negotiation, Springer, vol. 22(6), pages 1103-1127, November.

Books

  1. Lu, Jie, 2015. "Varieties of Governance in China: Migration and Institutional Change in Chinese Villages," OUP Catalogue, Oxford University Press, number 9780199378746.

    Cited by:

    1. Mobo C. F. Gao, 2017. "Vital factors for Chinese rural development: the reach of the state and lineage identity in villages," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 22(4), pages 547-559, October.
    2. Siwale, Agatha, 2018. "Can rural producer organisations transform rural production and trade? The case of Zambia's artisanal and small-scale mining associations," Resources Policy, Elsevier, vol. 59(C), pages 506-515.

  2. Jie Lu & Guangquan Zhang & Da Ruan & Fengjie Wu, 2007. "Multi-Objective Group Decision Making:Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM)," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number p505, December.

    Cited by:

    1. Branimir Stanić & Vladan Tubić & Nikola Čelar, 2011. "Design and evaluation of a grade-separated intersection: a case study of the proposed Belgrade ‘Hipodrom’," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(6), pages 625-636, June.
    2. Liu, Fang & Chen, Ya-Ru & Zhou, Da-Hai, 2023. "A two-dimensional approach to flexibility degree of XOR numbers with application to group decision making," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 267-287.
    3. Álvaro Labella & Rosa M. Rodríguez & Ahmad A. Alzahrani & Luis Martínez, 2020. "A Consensus Model for Extended Comparative Linguistic Expressions with Symbolic Translation," Mathematics, MDPI, vol. 8(12), pages 1-22, December.
    4. Farhad Shams & Sherif Mohamed & Aminah Robinson Fayek, 2014. "Improving Consistency Evaluation In Fuzzy Multi-Attribute Pairwise Comparison-Based Decision-Making Methods," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 31(04), pages 1-23.
    5. Jie Lu & Chenggen Shi & Guangquan Zhang & Da Ruan, 2007. "An Extended Branch And Bound Algorithm For Bilevel Multi-Follower Decision Making In A Referential-Uncooperative Situation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 371-388.

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