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Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector

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Abstract

Machine learning and artificial intelligence are the two fields of computer science dealing with the innovative idea of inducing smartness and intelligence in machines and automating complex tasks and operations through modern learning algorithms. While the rest of the operational fields have been diligent in developing new technologies, the mining industry has been lacking when it comes to applying these innovative methodologies to achieve operation autonomy with intelligence. However, this trend is beginning to change with a few researchers adopting the fields of machine learning and artificial intelligence to improve the existing technologies. This study was an attempt to review and analyze all the recent automation related work in every sector of the mining industry including mineral prospecting and exploration, mine planning, equipment selection, underground and surface equipment operation, drilling and blasting, mineral processing, etc., for establishing the existing frontiers of technological advancement. Shortcomings and challenges were identified within the current research work. Recommendations were provided to progress the existing technology by implementing deep learning, machine learning, and artificial intelligence for smart and intelligence-based evolution in the mining sector. With all of this innovative development and implementation of smart automation systems, the foundation for the mine of the future could be built, thus creating efficient, effective, and safer machines with sustainable mining operations.

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References

  • Aghajani Bazzazi ABBAS, Osanloo M, Karimi B (2011) A new fuzzy multi criteria decision making model for open pit mines equipment selection. Asia-Pacific J Oper Res 28:279–300

    MathSciNet  MATH  Google Scholar 

  • Ahmadi A, Hosseini MR (2015) A fuzzy logic model to predict the bioleaching efficiency of copper concentrates in stirred tank reactors. Int J Nonferrous Metall 4:1–8. https://doi.org/10.4236/ijnm.2015.41001

    Article  Google Scholar 

  • Ahamed NU, Yusof Z, Hamedon Z, Rabbi MF, Sikandar T, Palaniappan R, Ali MA, Rahman SM, Sundaraj K (2016) Fuzzy logic controller design for intelligent drilling system. In: 2016 IEEE international conference on automatic control and intelligent systems (I2CACIS). IEEE, pp 208–213

  • Al-Alawi SM, Tawo EE (1998) Application of artificial neural networks in mineral resource evaluation. J King Saud Univ Sci 10:127–138

    Google Scholar 

  • Aldinger JA, Kenny JM, Keran CM (1995) Mobile equipment accidents in surface coal mines. US Bur Mines Inf Circ 9428:51

    Google Scholar 

  • Ali D, Frimpong S (2018) Artificial intelligence models for predicting the performance of hydro-pneumatic suspension struts in large capacity dump trucks. Int J Ind Ergon 67:283–295. https://doi.org/10.1016/j.ergon.2018.06.005

    Article  Google Scholar 

  • Ali D, Hayat MB, Alagha L, Molatlhegi O (2018) An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal. Adv Powder Technol 29:3493–3506

    Google Scholar 

  • Anis M, Idrus A, Amijaya H, Subagyo S (2017) Fuzzy logic approach for post-mining land use planning: a case study on coal mine of Pt. Adaro Indonesia-South Kalimantan. Indones Min J 20:81–91

    Google Scholar 

  • Awuah-Offei K, Ur Rehman AU (2018) Investigating the information diffusion potential of social media networks. Min Eng SME 70:28–32

    Google Scholar 

  • Azar RE, Dickinson S, McCabe B (2012) Server-customer interaction tracker: computer vision–based system to estimate dirt-loading cycles. J Constr Eng Manag 139:785–794

    Google Scholar 

  • Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27:177–181

    Google Scholar 

  • Bandopadhyay S, Chattopadhyay A (1986) Selection of post-mining uses of land via fuzzy algorithm. In: 19th international symposium on applied computer mine planning, pp 321–332

  • Bangian AH, Ataei M, Sayadi A, Gholinejad A (2011) Fuzzy analytical hierarchy processing to define optimum post mining land use for pit area to clarify reclamation costs. Gospod Surowcami Miner 27:145–168

    Google Scholar 

  • Bartos PJ (2007) Is Mining a high-tech industry? Investigations into innovation and productivity advance. Resur Policy 32:149–158

    Google Scholar 

  • Bascetin A, Oztas O, Kanli AI (2006) EQS: a computer software using fuzzy logic for equipment selection in mining engineering. J S Afr Inst Min Metall 106:63

    Google Scholar 

  • Bekat T, Erdogan M, Inal F, Genc A (2012) Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks. Energy 45:882–887. https://doi.org/10.1016/j.energy.2012.06.075

    Article  Google Scholar 

  • Bellamy D, Pravica L (2011) Assessing the impact of driverless haul trucks in Australian Surface Mining. Resour Policy 36:149–158

    Google Scholar 

  • Bokhari AA, Hu GD, Al-Mokredi M, Benomar T, Diallo Y (2010) Fuzzy logic plateful mineral exploration and development in Southwestern China. In: 2010 2nd International conference information engineering computer science. IEEE, pp 1–4

  • Bradley DA, Seward DW (1998) The development, control and operation of an autonomous robotic excavator. J Intell Robot Syst 21:73–97

    Google Scholar 

  • Brown WM, Gedeon TD, Groves DI, Barnes RG (2000) Artificial neural networks: a new method for mineral prospectivity mapping. Aust J Earth Sci 47:757–770

    Google Scholar 

  • Brown WM, Groves DI, Gedeon TD (2003) An artificial neural network method for mineral prospectivity mapping: a comparison with fuzzy logic and Bayesian probability methods. Springer, Dordrecht

    Google Scholar 

  • Bullock DM, Oppenheim IJ (1989) A laboratory study of force-cognitive excavation. In: Proceedings sixth international symposium on automation and robotics in construction, pp 64–72.

  • Bügler M, Borrmann A, Ogunmakin G, Vela PA, Teizer J (2017) Fusion of photogrammetry and video analysis for productivity assessment of earthwork processes. Comput Civ Infrastruct Eng 32:107–123

    Google Scholar 

  • Cheng J, Li Y, Zhou J, Liu J, Cen K (2010) Maximum solid concentrations of coal water slurries predicted by neural network models. Fuel Process Technol 91:1832–1838. https://doi.org/10.1016/j.fuproc.2010.08.007

    Article  Google Scholar 

  • Chi S, Caldas CH (2011) Automated object identification using optical video cameras on construction sites. Comput Civ Infrastruct Eng 26:368–380

    Google Scholar 

  • Corke PI, Winstanley GJ, Roberts JM (1997) Dragline modelling and control. In: 1997 IEEE international conference robotics automation proceedings, IEEE

  • Corke P, Winstanley G, Dunbabin M, Roberts J (2003) Dragline automation: experimental evaluation through productivity trial. In: Field and service robotics 2003. Springer, Berlin, pp. 459–468.

  • DOE, Mining Industry Profile, (2019). https://www.energy.gov/eere/amo/mining-industry-profile. Accessed 10 July 2019

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) 2005. IEEE, vol 1, pp 886–893.

  • Demirbaş A (2002) Demineralization and desulfurization of coals via column froth flotation and different methods. Energy Convers Manag 43:885–895. https://doi.org/10.1016/S0196-8904(01)00088-7

    Article  Google Scholar 

  • Desai CK, Shaikh AA (2006) Drill wear monitoring using artificial neural network with differential evolution learning. In: 2006 IEEE international conference industry technology, IEEE, pp. 2019–2022

  • Dindarloo SR (2016) Dynamic impact of ageing dump truck suspension systems on whole-body vibrations in high-impact shovel loading operations. Missouri University of Science and Technology, Rolla

    Google Scholar 

  • Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75:27–36

    Google Scholar 

  • Elkatatny S, Tariq Z, Mahmoud M (2016) Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box). J Pet Sci Eng 146:1202–1210

    Google Scholar 

  • Erol M, Colduroglu C, Aktas Z (2003) The effect of reagents and reagent mixtures on froth flotation of coal fines. Int J Miner Process 71:131–145. https://doi.org/10.1016/S0301-7516(03)00034-6

    Article  Google Scholar 

  • Feng Q, Zhang J, Zhang X, Wen S (2015) Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Process Technol 129:120–129. https://doi.org/10.1016/j.fuproc.2014.09.001

    Article  Google Scholar 

  • Ghafoor A, Balakrishnan SN, Yucelen T (2018a) Modified state observer based decentralized neuro-adaptive controller for large scale interconnected uncertain systems. In: 2018 Annual American Control Conference, Milwaukee, WI, USA8, pp 1701–1706

  • Ghafoor A, Balakrishnan SN, Jagannathan S, Yucelen T (2018b) Event triggered neuro-adaptive controller (ETNAC) design for uncertain linear systems. In: 2018 IEEE conference on decision and control (CDC) 2018. IEEE, pp 2217–2222

  • Ghafoor A, Galchenko P, Balakrishnan SN, Pernicka H, Yucelen T (2019) ETNAC design enabling formation flight at liberation points. In: IEEE American Control Conference 2019, Philadelphia, PA, USA, pp 3689–3694

  • Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19:755–770

    Google Scholar 

  • Girmscheid G, Wälti R (2000) High performance drill and blast method-progress in efficiency through industrialized backup systems and process configuration. In: Progress in Tunnelling after 2000, vol 2, pp 163–171

  • Gocho T, Yamabe N, Hamaguchil T, Hitomi S, Ono T, Oshima H, Mori M, Fukuda M (1992) Automatic wheel-loader in asphalt plant. In: Proceeding the 9th international symposium automation and robotics in construction, pp 803–812

  • Golparvar-Fard M, Heydarian A, Niebles JC (2013) Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers. Adv Eng Inform 27:652–663

    Google Scholar 

  • Gonzalez GD, Orchard M, Cerda JL, Casali A, Vallebuona G (2003) Local models for soft-sensors in a rougher flotation bank. Miner Eng 16:441–453. https://doi.org/10.1016/S0892-6875(03)00021-9

    Article  Google Scholar 

  • Han C (1983) Coal cleaning by froth flotation, Iowa State University

  • Hainsworth DW (1996) Dragline Automation. In: Australian coal association research program project report, (C3007)

  • Hargrave JM, Hall ST (1997) Diagnosis of concentrate grade and mass flowrate in tin flotation from colour and surface texture analysis. Miner Eng 10:613–621. https://doi.org/10.1016/S0892-6875(97)00040-X

    Article  Google Scholar 

  • Hashmi K, Graham ID, Mills B (2000) Fuzzy logic based data selection for the drilling process. J Mater Process Technol 108:55–61

    Google Scholar 

  • Hayat MB (2018) Performance, Mitigation of environmental hazards of sulfide mineral flotation with an insight into froth stability and flotation. Dissertation for Missouri University of Science & Technology at Rolla MO, USA

  • Honaker RQ, Mohanty MK (1996) Enhanced column flotation performance for fine coal cleaning. Miner Eng 9:931–945. https://doi.org/10.1016/0892-6875(96)00085-4

    Article  Google Scholar 

  • Hosseini SH, Samanipour M (2015) Prediction of final concentrate grade using artificial neural networks from Gol-E-Gohar iron ore plant. Am J Min Metall 3(3):58–62. https://doi.org/10.12691/AJMM-3-3-1

    Article  Google Scholar 

  • Hosseini SAA, Ataei M, Hosseini SM, Akhyani M (2012) Application of fuzzy logic for determining of coal mine mechanization. J Coal Sci Eng 18:225–230

    Google Scholar 

  • Huang X, Bernold (1993) Robotic rock handling during backhoe excavation. In: 10th automation and robotics in construction, happened at Houston, USA from 24 to 26 May, 1993. Elsevier Science Publisher

  • Iphar M, Cukurluoz AK (2018) Fuzzy risk assessment for mechanized underground coal mines in Turkey. Int J Occup Saf Erg. https://doi.org/10.1080/10803548.2018.1426804

    Article  Google Scholar 

  • Jahedsaravani A, Marhaban MH, Massinaei M (2016) Application of statistical and intelligent techniques for modeling of metallurgical performance of a batch flotation process. Chem Eng Commun 203:151–160. https://doi.org/10.1080/00986445.2014.973944

    Article  Google Scholar 

  • Jang H, Topal E (2013) Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network. Tunn Undergr Sp Technol 38:161–169

    Google Scholar 

  • Ji W, Tang L, Li D, Yang W, Liao Q (2016) Video-based construction vehicles detection and its application in intelligent monitoring system. CAAI Trans Intell Technol 1:162–172

    Google Scholar 

  • Jorjani E, Asadollahi Poorali H, Sam A, Chehreh Chelgani S, Mesroghli S, Shayestehfar MR (2009) Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network. Miner Eng 22:970–976. https://doi.org/10.1016/j.mineng.2009.03.003

    Article  Google Scholar 

  • Karri V (1999) RBF neural network for thrust and torque predictions in drilling operations. In: Proceedings third international conference on computational intelligence and multimedia applications. ICCIMA'99 (Cat. No. PR00300) 1999, pp 55–59. IEEE.

  • Karadogan A, Kahriman A, Ozer U (2008) Application of fuzzy set theory in the selection of underground mining method. J South African Inst Min Metall 108:73–79

    Google Scholar 

  • Kashani SBM, Abedi M, Norouzi GH (2016) Fuzzy logic mineral potential mapping for copper exploration using multi-disciplinary geo-datasets, a case study in seridune deposit, Iran. Earth Sci Inform 9:167–181

    Google Scholar 

  • Keys R (1986) Promoters for froth flotation of coal, US Patient 4,589,980

  • Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

    Google Scholar 

  • Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blast-induced ground vibration. Eng Comput 27:117–125

    Google Scholar 

  • Kommadath B, Sarkar R, Rath B (2012) A fuzzy logic based approach to assess sustainable development of the mining and minerals sector. Sustain Dev 20:386–399

    Google Scholar 

  • Lashari SZ, Takbiri-Borujeni A, Fathi E, Sun T, Rahmani R, Khazaeli M (2019) Drilling performance monitoring and optimization: a data-driven approach. J Pet Explor Prod Technol 9:2747–2756

    Google Scholar 

  • Lee S, Oh HJ (2011) Application of artificial neural network for mineral potential mapping, IntechOpen

  • Marais C (2010) Estimation of concentrate grade in platinum flotation based on froth image analysis, University of Stellenbosch

  • Memarzadeh M, Golparvar-Fard M, Nieblesc JC (2013) Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Autom Constr 32:24–37

    Google Scholar 

  • Monjezi M, Singh TN, Khandelwal M, Sinha S, Singh V, Hosseini I (2006) Prediction and analysis of blast parameters using artificial neural network. Noise Vib Worldw 37:8–16

    Google Scholar 

  • Monjezi M, Rezaei M, Varjani AY (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46:1273–1280

    Google Scholar 

  • Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37:2637–2643

    Google Scholar 

  • Mukhopadhjay A (1989) Selection, maintenance, and relations of various parameters for off-highway hauling tires. Off-Highway Haulage in Surface Mines, Ed. Golosinski, Sraje, Balkema, pp 153–159

  • Nakhaei F, Mosavi MR, Sam A, Vaghei Y (2012) Recovery and grade accurate prediction of pilot plant flotation column concentrate: neural network and statistical techniques. Int J Miner Process 110–111:140–154. https://doi.org/10.1016/j.minpro.2012.03.003

    Article  Google Scholar 

  • Narendran TV, Weinelt B (2017) Digital transformation initiative mining and metals industry, World Econ. Forum. https://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/wef-dti-mining-and-metals-white-paper.pdf. Aaccessed 10 June 2019.

  • Nebot EM (2007) Surface mining: main research issues for autonomous operations. In: Robotics Research. Springer, Berlin, pp 268–280

  • Nykänen V, Groves DI, Ojala VJ, Eilu P, Gardoll SJ (2008) Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper–gold deposits in the northern Fennoscandian Shield, Finland. Aust J Earth Sci 55:25–38

    Google Scholar 

  • Ozkan E, Iphar M, Konuk A (2019) Fuzzy logic approach in resource classification. Int J Min Reclam Environ 33:183–205

    Google Scholar 

  • Parreira J (2013) An Interactive simulation model to compare an autonomous haulage truck system with a manually-operated system. University of British Columbia, Vancouver

    Google Scholar 

  • Pendokhare DG, Quazi TZ (2012) Fuzzy Logic Based Drilling Control Process. Int J Sci Eng Res 5:61–65

    Google Scholar 

  • Pusat S, Akkoyunlu MT, Pekel E, Akkoyunlu MC, Özkan C, Kara SS (2016) Estimation of coal moisture content in convective drying process using ANFIS. Fuel Process Technol 147:12–17. https://doi.org/10.1016/j.fuproc.2015.12.010

    Article  Google Scholar 

  • Rahman A, Dutta R, Smith D (2013) Exploring mineral domains with genetic algorithm. In: 2013 international JT conference neural networks. IEEE, pp 1–8

  • Ramani RV (2012) Surface mining technology: progress and prospects. Procedia Eng 46:9–21

    Google Scholar 

  • Rezaei M, Monjezi M, Varjani AY (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49:298–305

    Google Scholar 

  • Rigol-Sanchez JP, Chica-Olmo M, Abarca-Hernandez F (2003) Artificial neural networks as a tool for mineral potential mapping with GIS. Int J Remote Sens 24:1151–1156

    Google Scholar 

  • Roberts JM, Corke PI, Winstanley GJ (1999) Development of a 3500-tonne field robot. Int J Rob Res 18:739–752

    Google Scholar 

  • Roberts JM, Duff ES, Corke PI, Sikka P, Winstanley GJ, Cunningham J (2000) Autonomous control of underground mining vehicles using reactive navigation. In: Proceedings 2000 ICRA. Millennium Conference. IEEE international conference on robotics and automation. symposia proceedings (Cat. No. 00CH37065) 2000 Apr 24. IEEE, vol 4, pp 3790–3795

  • Ruff T (2002) Hazard detection and warning devices: safety enhancement for off-highway dump trucks a compend. NIOSH Mining Reserch, Washington DC

    Google Scholar 

  • Ruff T, Coleman P, Martini L (2011) Machine-related injuries in the US mining industry and priorities for safety research. Int J Inj Contr Saf Promot 18:11–20. https://doi.org/10.1080/17457300.2010.487154

    Article  Google Scholar 

  • Saravani AJ, Mehrshad N, Massinaei M (2014) Fuzzy-based modelling and control of an industrial flotation column. Chem Eng Commun 201:896–908. https://doi.org/10.1080/00986445.2013.790815

    Article  Google Scholar 

  • Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5:318–324

    Google Scholar 

  • Setyadi H, Widodo LE, Notosiswoyo S, Saptawati P, Ismanto A, Hardjana I (2016) GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data. In: AIP conference proceedings, pp 1711(1).

  • Shi X, Lever PJ, Wang F-Y (1996) Experimental robotic excavation with fuzzy logic and neural networks. In: 1996 IEEE international conference proceedings robotics automation, IEEE

  • Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832

    Google Scholar 

  • Somua-Gyimah G, Frimpong S, Nyaaba W, Gbadam E (2019) A computer vision system for terrain recognition and object detection tasks in mining and construction environments. In: SME Annual Conference

  • Sukkarieh S, Eduardo Mario N, D.-W. Hugh F. (1999) A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Trans Robot Autom 15:572–578

    Google Scholar 

  • Tabaei M, Esfahani MM, Rasekh P, Esna-ashari A (2017) Mineral prospectivity mapping in GIS using fuzzy logic integration in Khondab area, western Markazi province, Iran. J Tethys 5:367–379

    Google Scholar 

  • Thompson D (1999) Modern drilling and blasting technology at the Minntac mine. In: MINNBLAST 99, Duluth, Minnesota, USA, 1999, pp 79–96

  • Tiile RN (2018) Artificial neural network approach to predict blast-induced ground vibration, airblast and rock fragmentation vibration, airblast and rock fragmentation. Missouri University of Science and Technology

  • Torno S, Toraño J, Menéndez M, Gent M, Velasco J (2011) Mathematical and fuzzy logic models in prediction of geological and geomechanical properties of rock mass by excavation data on underground works. J Civ Eng Manag 17:197–206

    Google Scholar 

  • Trivedi R, Singh TN, Mudgal K, Gupta N (2014) Application of artificial neural network for blast performance evaluation. Int J Res Eng Technol 3:564–574

    Google Scholar 

  • Wang JS, Han S (2015) Feed-Forward neural network soft-sensor modeling of flotation process based on particle swarm optimization and gravitational search algorithm. Comput Intell Neurosci 1:1

    Google Scholar 

  • Wei ZHOU, Cai QX, Chen SZ (2007) Study on dragline-bulldozer operation with variations in coal seam thickness. J China Univ Min Technol 17:464–466

    Google Scholar 

  • Winstanley G, Corke PI, Roberts J (1999) Dragline Automation Implementation. In: Australian coal association research program project report, (C5003)

  • Yang H, Zhao S (2010) Prediction model for production indexes of a flotation circuit based on adaptive PCA and composite kernel support vector regression. In: 2010 International conference on biomedical engineering and computer science. IEEE, pp 1–4

  • Zhang N, Zhou K (2015) Mineral prospectivity mapping with weights of evidence and fuzzy logic methods. J Intell Fuzzy Syst 29:2639–2651

    Google Scholar 

  • Zhongya Z, Xiaoguang J (2018) Prediction of peak velocity of blasting vibration based on artificial neural network optimized by dimensionality reduction of FA-MIV. In: Mathematical Problems in Engineering.

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  1. Department of Mining and Nuclear Engineering, Missouri University of Science and Technology, Rolla, MO, USA

    Danish Ali & Samuel Frimpong

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Ali, D., Frimpong, S. Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artif Intell Rev 53, 6025–6042 (2020). https://doi.org/10.1007/s10462-020-09841-6

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