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Journal articles on the topic 'Data-Driven Processes'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Bonné, Dennis, and Sten Bay Jørgensen. "Data-Driven Modeling of Batch Processes." IFAC Proceedings Volumes 37, no. 1 (January 2004): 589–94. http://dx.doi.org/10.1016/s1474-6670(17)38796-7.

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2

Van Ameijde, Jeroen. "Data-driven Urban Design." SPOOL 9, no. 1 (May 27, 2022): 35–48. http://dx.doi.org/10.47982/spool.2022.1.03.

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Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable data-driven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces.
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3

Lickert, Benjamin, Steffen Wolf, and Gerhard Stock. "Data-Driven Langevin Modeling of Nonequilibrium Processes." Journal of Physical Chemistry B 125, no. 29 (July 16, 2021): 8125–36. http://dx.doi.org/10.1021/acs.jpcb.1c03828.

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4

Kamgaing, Joseph Tadjuidje, Hernando Ombao, and Richard A. Davis. "Autoregressive processes with data-driven regime switching." Journal of Time Series Analysis 30, no. 5 (September 2009): 505–33. http://dx.doi.org/10.1111/j.1467-9892.2009.00622.x.

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5

Boukouvala, F., F. J. Muzzio, and Marianthi G. Ierapetritou. "Dynamic Data-Driven Modeling of Pharmaceutical Processes." Industrial & Engineering Chemistry Research 50, no. 11 (June 2011): 6743–54. http://dx.doi.org/10.1021/ie102305a.

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6

Liang, Y. C., S. Wang, W. D. Li, and X. Lu. "Data-Driven Anomaly Diagnosis for Machining Processes." Engineering 5, no. 4 (August 2019): 646–52. http://dx.doi.org/10.1016/j.eng.2019.03.012.

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7

Jiang, Qingchao, Huaikuan Yi, Xuefeng Yan, Xinmin Zhang, and Jian Huang. "Data-Driven Model Predictive Monitoring for Dynamic Processes." IFAC-PapersOnLine 53, no. 2 (2020): 98–103. http://dx.doi.org/10.1016/j.ifacol.2020.12.101.

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8

Wiebe, Johannes, Inês Cecílio, and Ruth Misener. "Data-Driven Optimization of Processes with Degrading Equipment." Industrial & Engineering Chemistry Research 57, no. 50 (November 16, 2018): 17177–91. http://dx.doi.org/10.1021/acs.iecr.8b03292.

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9

Hasenauer, Jan, Nick Jagiella, Sabrina Hross, and Fabian J. Theis. "Data-Driven Modelling of Biological Multi-Scale Processes." Journal of Coupled Systems and Multiscale Dynamics 3, no. 2 (June 1, 2015): 101–21. http://dx.doi.org/10.1166/jcsmd.2015.1069.

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10

Hashtroudi, Shahin, Susan A. Ferguson, Virginia A. Rappold, and Linda D. Chrosniak. "Data-driven and conceptually driven processes in partial-word identification and recognition." Journal of Experimental Psychology: Learning, Memory, and Cognition 14, no. 4 (1988): 749–57. http://dx.doi.org/10.1037/0278-7393.14.4.749.

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11

Tenpenny, Patricia L., and Edward J. Shoben. "Component processes and the utility of the conceptually-driven/data-driven distinction." Journal of Experimental Psychology: Learning, Memory, and Cognition 18, no. 1 (1992): 25–42. http://dx.doi.org/10.1037/0278-7393.18.1.25.

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12

Haider, Hilde, Peter A. Frensch, and Daniel Joram. "Are strategy shifts caused by data-driven processes or by voluntary processes?" Consciousness and Cognition 14, no. 3 (September 2005): 495–519. http://dx.doi.org/10.1016/j.concog.2004.12.002.

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13

Bassili, John N., Marilyn C. Smith, and Colin M. MacLeod. "Auditory and Visual Word-Stem Completion: Separating Data-Driven and Conceptually Driven Processes." Quarterly Journal of Experimental Psychology Section A 41, no. 3 (August 1989): 439–53. http://dx.doi.org/10.1080/14640748908402375.

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Two experiments investigated the contributions of data-driven and conceptually driven processing on an implicit word-stem completion task. In Experiment 1, individual words were studied either visually or auditorily and were tested using either visual or auditory word-stems. Keeping modality the same from study to test led to more priming than did changing modality, but there was reliable cross-modal priming. In Experiment 2, subjects read sentences like The boat travelled underwater and inferred the subject noun (i.e. “submarine”) or sentences like The submarine travelled underwater and categorized the subject noun (i.e. “boat”). At test, there was reliable priming for both actually read nouns and inferred nouns. In addition, a modality effect was evident for the actually read nouns but not for the inferred nouns. Taken together, these results imply that there is a basic conceptually driven contribution to priming plus an additional contribution of data-driven processing when surface form is the same at study and test.
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14

Marino, Daniel L., and Milos Manic. "Physics Enhanced Data-Driven Models With Variational Gaussian Processes." IEEE Open Journal of the Industrial Electronics Society 2 (2021): 252–65. http://dx.doi.org/10.1109/ojies.2021.3064820.

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15

Kavrakov, Igor, Allan McRobie, and Guido Morgenthal. "Data-driven aerodynamic analysis of structures using Gaussian Processes." Journal of Wind Engineering and Industrial Aerodynamics 222 (March 2022): 104911. http://dx.doi.org/10.1016/j.jweia.2022.104911.

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16

Zhang, Jianhua, Xiao Tian, Zhengmao Zhu, and Mifeng Ren. "Data-Driven Superheating Control of Organic Rankine Cycle Processes." Complexity 2018 (October 21, 2018): 1–8. http://dx.doi.org/10.1155/2018/4154019.

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In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle (ORC) processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy (QMEE) is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization (PSO) algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. The simulation results testify the effectiveness of the presented control algorithm.
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17

Han, Shuo, Victor M. Preciado, Cameron Nowzari, and George J. Pappas. "Data-Driven Network Resource Allocation for Controlling Spreading Processes." IEEE Transactions on Network Science and Engineering 2, no. 4 (October 1, 2015): 127–38. http://dx.doi.org/10.1109/tnse.2015.2500158.

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18

Ritter, Tobias, Juliane Euler, Stefan Ulbrich, and Oskar von Stryk. "Decentralized Dynamic Data-driven Monitoring of Atmospheric Dispersion Processes." Procedia Computer Science 80 (2016): 919–30. http://dx.doi.org/10.1016/j.procs.2016.05.382.

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19

Bonné, Dennis, and Sten Bay Jørgensen. "Data-Driven Modeling of Nonlinear and Time-Varying Processes." IFAC Proceedings Volumes 36, no. 16 (September 2003): 1615–20. http://dx.doi.org/10.1016/s1474-6670(17)34991-1.

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20

Sadati, Najibesadat, Ratna Babu Chinnam, and Milad Zafar Nezhad. "Observational data-driven modeling and optimization of manufacturing processes." Expert Systems with Applications 93 (March 2018): 456–64. http://dx.doi.org/10.1016/j.eswa.2017.10.028.

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21

Aumi, Siam, Brandon Corbett, Tracy Clarke-Pringle, and Prashant Mhaskar. "Data-driven model predictive quality control of batch processes." AIChE Journal 59, no. 8 (March 27, 2013): 2852–61. http://dx.doi.org/10.1002/aic.14063.

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22

Wang, Zhenyu, and Christos Georgakis. "Anin silicoevaluation of data-driven optimization of biopharmaceutical processes." AIChE Journal 63, no. 7 (February 10, 2017): 2796–805. http://dx.doi.org/10.1002/aic.15659.

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23

Yang, Qiangda, Hongbo Gao, Weijun Zhang, Zhongyuan Chi, and Zhi Yi. "A new data-driven modeling method for fermentation processes." Chemometrics and Intelligent Laboratory Systems 152 (March 2016): 88–96. http://dx.doi.org/10.1016/j.chemolab.2016.01.013.

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24

Quiñones-Grueiro, Marcos, Alberto Prieto-Moreno, Cristina Verde, and Orestes Llanes-Santiago. "Data-driven monitoring of multimode continuous processes: A review." Chemometrics and Intelligent Laboratory Systems 189 (June 2019): 56–71. http://dx.doi.org/10.1016/j.chemolab.2019.03.012.

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25

Bradford, Eric, Lars Imsland, Dongda Zhang, and Ehecatl Antonio del Rio Chanona. "Stochastic data-driven model predictive control using gaussian processes." Computers & Chemical Engineering 139 (August 2020): 106844. http://dx.doi.org/10.1016/j.compchemeng.2020.106844.

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26

Liu, Jialin. "Data-driven fault detection and isolation for multimode processes." Asia-Pacific Journal of Chemical Engineering 6, no. 3 (February 10, 2011): 470–83. http://dx.doi.org/10.1002/apj.549.

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27

Schröder, Patrick, Arndt Wagner, Daniela Stöhr, Markus Rehm, and Wolfgang Ehlers. "Data-driven simulation of metastatic processes within brain tissue." PAMM 17, no. 1 (December 2017): 221–22. http://dx.doi.org/10.1002/pamm.201710080.

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28

Chang, Haibin, and Dongxiao Zhang. "Identification of physical processes via combined data-driven and data-assimilation methods." Journal of Computational Physics 393 (September 2019): 337–50. http://dx.doi.org/10.1016/j.jcp.2019.05.008.

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29

Amini, Mohammadhossein, and Shing I. Chang. "Intelligent data-driven monitoring of high dimensional multistage manufacturing processes." International Journal of Mechatronics and Manufacturing Systems 13, no. 4 (2020): 299. http://dx.doi.org/10.1504/ijmms.2020.112352.

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30

Krüger, M., B. Vogel-Heuser, I. Weiß, and E. Trunzer. "Data-Driven Product Quality Monitoring in Quality-Critical Forming Processes." IFAC-PapersOnLine 54, no. 4 (2021): 220–25. http://dx.doi.org/10.1016/j.ifacol.2021.10.037.

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31

Qin, S. Joe. "Data-driven Fault Detection and Diagnosis for Complex Industrial Processes." IFAC Proceedings Volumes 42, no. 8 (2009): 1115–25. http://dx.doi.org/10.3182/20090630-4-es-2003.00184.

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32

Chang, Shing I., and Mohammadhossein Amini. "Intelligent data-driven monitoring of high dimensional multistage manufacturing processes." International Journal of Mechatronics and Manufacturing Systems 13, no. 4 (2020): 299. http://dx.doi.org/10.1504/ijmms.2020.10034619.

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33

Modoni, Gianfranco E., Marco Sacco, and Walter Terkaj. "A Telemetry-driven Approach to Simulate Data-intensive Manufacturing Processes." Procedia CIRP 57 (2016): 281–85. http://dx.doi.org/10.1016/j.procir.2016.11.049.

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34

Jia, Li, Luming Cao, and Minsen Chiu. "Data-driven Based Integrated Learning Controller Design for Batch Processes." IFAC Proceedings Volumes 45, no. 15 (2012): 234–38. http://dx.doi.org/10.3182/20120710-4-sg-2026.00050.

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35

Miletic, Ivan, François Boudreau, Michael Dudzic, Greg Kotuza, Laura Ronholm, Vit Vaculik, and Yale Zhang. "Experiences in applying data-driven modelling technology to steelmaking processes." Canadian Journal of Chemical Engineering 86, no. 5 (September 28, 2008): 937–46. http://dx.doi.org/10.1002/cjce.20090.

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36

Patil, Sunil A., Sylvia Gildemyn, Deepak Pant, Karsten Zengler, Bruce E. Logan, and Korneel Rabaey. "A logical data representation framework for electricity-driven bioproduction processes." Biotechnology Advances 33, no. 6 (November 2015): 736–44. http://dx.doi.org/10.1016/j.biotechadv.2015.03.002.

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37

Hao, Haiyang, Kai Zhang, Steven X. Ding, Zhiwen Chen, and Yaguo Lei. "A data-driven multiplicative fault diagnosis approach for automation processes." ISA Transactions 53, no. 5 (September 2014): 1436–45. http://dx.doi.org/10.1016/j.isatra.2013.12.022.

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38

Boukouvala, Fani, Fernando J. Muzzio, and Marianthi G. Ierapetritou. "Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods." Journal of Pharmaceutical Innovation 5, no. 3 (September 14, 2010): 119–37. http://dx.doi.org/10.1007/s12247-010-9086-y.

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39

Biagi, Vittoria, and Angela Russo. "Data Model Design to Support Data-Driven IT Governance Implementation." Technologies 10, no. 5 (October 8, 2022): 106. http://dx.doi.org/10.3390/technologies10050106.

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Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.
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40

D’Hauwers, R., K. Borghys, J. T. A. Vannieuwenhuyze, N. Walravens, and B. Lievens. "CHALLENGES IN DATA-DRIVEN POLICYMAKING: USING SMART CITY DATA TO SUPPORT LOCAL RETAIL POLICIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-4/W2-2020 (September 15, 2020): 55–61. http://dx.doi.org/10.5194/isprs-annals-vi-4-w2-2020-55-2020.

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Abstract. In Flanders (Belgium), there is a growing awareness among city administrations and governments to turn their cities into smart cities by transforming their policy decisions into evidence-based and data-driven policymaking. Nonetheless, many Flemish cities still face several challenges related to the integration of data-driven policy evaluation research at different stages of their policy making processes. For that reason, the ‘Smart Retail Dashboard’-project was set up, which aims to develop a dashboard for helping Flemish cities in transforming their retail policies into data-driven policies. This paper reports on the initial findings of this project. The most important finding is the varying degrees of maturity across cities regarding their smart retail policies. This is illustrated by the current state of data-driven policy making in the retail sector in Flanders. Subsequently we provide an overview of the current challenges reported by mature and less mature cities, which range from the cost and the usability of data to the analysis and interpretation of such data. We discuss how a Smart Retail Dashboard could provide answers to these challenges. This discussion helps city administrations to transform their policymaking processes into data-driven processes and dashboard developers to optimise their development processes.
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41

Cermak, Laird S., Mieke Verfaellie, and Kenneth A. Chase. "Implicit and explicit memory in amnesia: An analysis of data-driven and conceptually driven processes." Neuropsychology 9, no. 3 (1995): 281–90. http://dx.doi.org/10.1037/0894-4105.9.3.281.

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42

Fiordalis, Andrew, and Christos Georgakis. "Data-driven, using design of dynamic experiments, versus model-driven optimization of batch crystallization processes." Journal of Process Control 23, no. 2 (February 2013): 179–88. http://dx.doi.org/10.1016/j.jprocont.2012.08.011.

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43

Preuveneers, Davy, Wouter Joosen, and Elisabeth Ilie-Zudor. "Trustworthy data-driven networked production for customer-centric plants." Industrial Management & Data Systems 117, no. 10 (December 4, 2017): 2305–24. http://dx.doi.org/10.1108/imds-10-2016-0419.

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Purpose Industry 4.0 envisions a future of networked production where interconnected machines and business processes running in the cloud will communicate with one another to optimize production and enable more efficient and sustainable individualized/mass manufacturing. However, the openness and process transparency of networked production in hyperconnected manufacturing enterprises pose severe cyber-security threats and information security challenges that need to be dealt with. The paper aims to discuss these issues. Design/methodology/approach This paper presents a distributed trust model and middleware for collaborative and decentralized access control to guarantee data transparency, integrity, authenticity and authorization of dataflow-oriented Industry 4.0 processes. Findings The results of a performance study indicate that private blockchains are capable of securing IoT-enabled dataflow-oriented networked production processes across the trust boundaries of the Industry 4.0 manufacturing enterprise. Originality/value This paper contributes a decentralized identity and relationship management for users, sensors, actuators, gateways and cloud services to support processes that cross the trust boundaries of the manufacturing enterprise, while offering protection against malicious adversaries gaining unauthorized access to systems, services and information.
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44

Helbich, Marco, Wolfgang Brunauer, Julian Hagenauer, and Michael Leitner. "Data-Driven Regionalization of Housing Markets." Annals of the Association of American Geographers 103, no. 4 (July 2013): 871–89. http://dx.doi.org/10.1080/00045608.2012.707587.

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45

Ziaei-Halimejani, Hooman, Reza Zarghami, Seyed Soheil Mansouri, and Navid Mostoufi. "Data-Driven Fault Diagnosis of Chemical Processes Based on Recurrence Plots." Industrial & Engineering Chemistry Research 60, no. 7 (February 10, 2021): 3038–55. http://dx.doi.org/10.1021/acs.iecr.0c06307.

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46

Clarke, Colin, and Cleo Kontoravdi. "Editorial overview: Mechanistic and data-driven modelling of biopharmaceutical manufacturing processes." Current Opinion in Chemical Engineering 37 (September 2022): 100844. http://dx.doi.org/10.1016/j.coche.2022.100844.

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47

Ramani, Sivaramakrishnan, and Archis Ghate. "Robust Markov Decision Processes with Data-Driven, Distance-Based Ambiguity Sets." SIAM Journal on Optimization 32, no. 2 (May 19, 2022): 989–1017. http://dx.doi.org/10.1137/21m1423841.

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48

Bernardo, Reginald Christian, and Jackson Levi Said. "A data-driven reconstruction of Horndeski gravity via the Gaussian processes." Journal of Cosmology and Astroparticle Physics 2021, no. 09 (September 1, 2021): 014. http://dx.doi.org/10.1088/1475-7516/2021/09/014.

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49

Ghahramani, Mohammadhossein, Yan Qiao, MengChu Zhou, Adrian O Hagan, and James Sweeney. "AI-based modeling and data-driven evaluation for smart manufacturing processes." IEEE/CAA Journal of Automatica Sinica 7, no. 4 (July 2020): 1026–37. http://dx.doi.org/10.1109/jas.2020.1003114.

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50

Artopoulos, Georgios, and Carlos Smaniotto Costa. "Data-Driven Processes in Participatory Urbanism: The “Smartness” of Historical Cities." Architecture and Culture 7, no. 3 (September 2, 2019): 473–91. http://dx.doi.org/10.1080/20507828.2019.1631061.

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