UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
The benchmarking of smart e-tourism data management applications falls under the problem of multicriteria decision-making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision-making framework on the basis of the extension of interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e-tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e-tourism data management applications of each category and subcategory in smart e-tourism. In the second phase, the integration of the IT2TR-FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e-tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real-time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context-awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e-tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed. ? 2021 Wiley Periodicals LLC |
References |
Abdar, M., Zomorodi-Moghadam, M., Das, R., & Ting, I. -. (2019). Corrigendum to “Performance analysis of classification algorithms on early detection of liver disease” (expert systems with applications (2017) 67 (239–251), (S095741741630464X) (10.1016/j.eswa.2016.08.065)). Expert Systems with Applications, 125, 442-443. doi:10.1016/j.eswa.2019.02.029 Abdulkareem, K. H., Arbaiy, N., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., & Salih, M. M. (2021). A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy delphi and hybrid multi-criteria decision analysis methods. Neural Computing and Applications, 33(4), 1029-1054. doi:10.1007/s00521-020-05020-4 Abdulkareem, K. H., Arbaiy, N., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., & Salih, M. M. (2020). A novel multi-perspective benchmarking framework for selecting image dehazing intelligent algorithms based on BWM and group VIKOR techniques. International Journal of Information Technology and Decision Making, 19(3), 909-957. doi:10.1142/S0219622020500169 Alaa, M., Albakri, I. S. M. A., Singh, C. K. S., Hammed, H., Zaidan, A. A., Zaidan, B. B., . . . Jasim, A. N. (2019). Assessment and ranking framework for the english skills of pre-service teachers based on fuzzy delphi and TOPSIS methods. IEEE Access, 7, 126201-126223. doi:10.1109/ACCESS.2019.2936898 Albahri, A. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, O. S., Hamid, R. A., Zaidan, B. B., . . . Hashim, M. (2020). Multi-biological laboratory examination framework for the prioritization of patients with COVID-19 based on integrated AHP and group VIKOR methods. International Journal of Information Technology and Decision Making, 19(5), 1247-1269. doi:10.1142/S0219622020500285 Albahri, A. S., Hamid, R. A., Albahri, O. S., & Zaidan, A. A. (2021). Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods. Artificial Intelligence in Medicine, 111 doi:10.1016/j.artmed.2020.101983 Albahri, A. S., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., & Alsalem, M. A. (2018). Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects. Journal of Medical Systems, 42(8) doi:10.1007/s10916-018-0983-9 Albahri, O. (2021). New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR. Journal of Ambient Intelligence and Humanized Computing, , 1-21. Retrieved from www.scopus.com Albahri, O. S., Albahri, A. S., Mohammed, K. I., Zaidan, A. A., Zaidan, B. B., Hashim, M., & Salman, O. H. (2018). Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: Taxonomy, open challenges, motivation and recommendations. Journal of Medical Systems, 42(5) doi:10.1007/s10916-018-0943-4 Albahri, O. S., Albahri, A. S., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Mohsin, A. H., . . . Shareef, A. H. (2019). Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access, 7, 50052-50080. doi:10.1109/ACCESS.2019.2910411 Albahri, O. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Salih, M. M., . . . Zulkifli, C. Z. (2020). Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Computer Methods and Programs in Biomedicine, 196 doi:10.1016/j.cmpb.2020.105617 Albahri, O. S., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Abdulkareem, K. H., Al-qaysi, Z. T., . . . Rashid, N. A. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health, 13(10), 1381-1396. doi:10.1016/j.jiph.2020.06.028 Albahri, O. S., Zaidan, A. A., Salih, M. M., Zaidan, B. B., Khatari, M. A., Ahmed, M. A., . . . Alazab, M. (2021). Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method. International Journal of Intelligent Systems, 36(2), 796-831. doi:10.1002/int.22322 Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-time remote health-monitoring systems in a medical centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems, 42(9) doi:10.1007/s10916-018-1006-6 Al-Hassan, M., Lu, H., & Lu, J. (2015). A semantic enhanced hybrid recommendation approach: A case study of e-government tourism service recommendation system. Decision Support Systems, 72, 97-109. doi:10.1016/j.dss.2015.02.001 Allwinkle, S., & Cruickshank, P. (2011). Creating smart-er cities: An overview. Journal of Urban Technology, 18(2), 1-16. doi:10.1080/10630732.2011.601103 Almahdi, E. M., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2019). Mobile patient monitoring systems from a benchmarking aspect: Challenges, open issues and recommended solutions. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1336-z Almahdi, E. M., Zaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2019). Mobile-based patient monitoring systems: A prioritisation framework using multi-criteria decision-making techniques. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1339-9 Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, O. S., Albahri, A. S., . . . Mohammed, K. I. (2018). Systematic review of an automated multiclass detection and classification system for acute leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. Journal of Medical Systems, 42(11) doi:10.1007/s10916-018-1064-9 Angskun, T., & Angskun, J. (2018). A qualitative attraction ranking model for personalized recommendations. Journal of Hospitality and Tourism Technology, 9(1), 2-12. doi:10.1108/JHTT-09-2016-0047 Arifin, Z., Ibrahim, M. R., & Hatta, H. R. (2017). Nearest tourism site searching using haversine method. Paper presented at the Proceedings - 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2016, 293-296. doi:10.1109/ICITACEE.2016.7892458 Retrieved from www.scopus.com Badi, I., & Pamucar, D. (2020). Supplier selection for steelmaking company by using combined grey-marcos methods. Decision Making: Applications in Management and Engineering, 3(2), 37-47. doi:10.31181/dmame2003037b Berger, H., Dittenbach, M., Merkl, D., Bogdanovych, A., Simoff, S., & Sierra, C. (2007). Opening new dimensions for e-tourism. Virtual Reality, 11(2-3), 75-87. doi:10.1007/s10055-006-0057-z Biswas, S. (2020). Measuring performance of healthcare supply chains in india: A comparative analysis of multi-criteria decision making methods. Decision Making: Applications in Management and Engineering, 3(2), 162-189. doi:10.31181/dmame2003162b Bodkhe, U., Bhattacharya, P., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2019). BloHosT: Blockchain enabled smart tourism and hospitality management. Paper presented at the CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems, doi:10.1109/CITS.2019.8862001 Retrieved from www.scopus.com Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41(16), 7370-7389. doi:10.1016/j.eswa.2014.06.007 Brandt, T., Bendler, J., & Neumann, D. (2017). Social media analytics and value creation in urban smart tourism ecosystems. Information and Management, 54(6), 703-713. doi:10.1016/j.im.2017.01.004 Buhalis, D., & Amaranggana, A. (2015). Smart tourism destinations enhancing tourism experience through personalisation of services. Information and Communication Technologies in Tourism 2015, , 377-389. Retrieved from www.scopus.com Burke, R. (2007). Hybrid web recommender systems doi:10.1007/978-3-540-72079-9_12 Retrieved from www.scopus.com Chang, S. E., & Shen, W. -. (2018). Exploring smartphone social networking services for mobile tourism. International Journal of Mobile Communications, 16(1), 63-81. doi:10.1504/IJMC.2018.088273 Colomo-Palacios, R., García-Peñalvo, F. J., Stantchev, V., & Misra, S. (2017). Towards a social and context-aware mobile recommendation system for tourism. Pervasive and Mobile Computing, 38, 505-515. doi:10.1016/j.pmcj.2016.03.001 Enaizan, O., Zaidan, A. A., Alwi, N. H. M., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2020). Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health and Technology, 10(3), 795-822. doi:10.1007/s12553-018-0278-7 Garcia, L. M., Aciar, S., Mendoza, R., & Puello, J. J. (2018). Smart tourism platform based on microservice architecture and recommender services doi:10.1007/978-3-319-97163-6_14 Retrieved from www.scopus.com Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., . . . Xiang, Z. (2020). e-tourism beyond COVID-19: A call for transformative research. Information Technology and Tourism, 22(2), 187-203. doi:10.1007/s40558-020-00181-3 Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: Foundations and developments. Electronic Markets, 25(3), 179-188. doi:10.1007/s12525-015-0196-8 Gruber, T. (2008). Collective knowledge systems: Where the social web meets the semantic web. Web Semantics, 6(1), 4-13. doi:10.1016/j.websem.2007.11.011 Hamid, R. A., Albahriac Jwan, A. S., & Alwand, K. (0000). How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management. Comput Sci Rev, 39 Retrieved from www.scopus.com Han, J., & Lee, H. (2015). Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media. Pervasive and Mobile Computing, 18, 4-17. doi:10.1016/j.pmcj.2014.08.002 Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., & Williams, P. (2010). Foundations for smarter cities. IBM Journal of Research and Development, 54(4) doi:10.1147/JRD.2010.2048257 He, C., Tian, Y., Jin, Y., Zhang, X., & Pan, L. (2017). A radial space division based evolutionary algorithm for many-objective optimization. Applied Soft Computing, 61, 603-621. doi:10.1016/j.asoc.2017.08.024 Ibrahim, N. K., Hammed, H., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., . . . Alaa, M. (2019). Multi-criteria evaluation and benchmarking for young learners' english language mobile applications in terms of LSRW skills. IEEE Access, 7, 146620-146651. doi:10.1109/ACCESS.2019.2941640 Jin, C., Cheng, J., & Xu, J. (2018). Using user-generated content to explore the temporal heterogeneity in tourist mobility. Journal of Travel Research, 57(6), 779-791. doi:10.1177/0047287517714906 Jorro-Aragoneses, J. L., Diaz Agudo, M. B., & Recio Garcia, J. A. (2018). Madrid live: A context-aware recomendar system of leisure plans. Paper presented at the Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, , 2017-November 796-801. doi:10.1109/ICTAI.2017.00125 Retrieved from www.scopus.com Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., Albahri, O. S., & Albahri, A. S. (2018). Based on real time remote health monitoring systems: A new approach for prioritization “Large scales data” patients with chronic heart diseases using body sensors and communication technology. Journal of Medical Systems, 42(4) doi:10.1007/s10916-018-0916-7 Kaya, T., & Kahraman, C. (2011). Fuzzy multiple criteria forestry decision making based on an integrated VIKOR and AHP approach. Expert Systems with Applications, 38(6), 7326-7333. doi:10.1016/j.eswa.2010.12.003 Kaya, T., & Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of istanbul. Energy, 35(6), 2517-2527. doi:10.1016/j.energy.2010.02.051 Khatari, M. (2020). Multidimensional benchmarking framework for AQMs of network congestion control based on AHP and group-TOPSIS. Int J Inf Technol Decis Mak, 19, 1-20. Retrieved from www.scopus.com Khatari, M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., & Alsalem, M. A. (2019). Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions. International Journal of Information Technology and Decision Making, 18(4), 1187-1242. doi:10.1142/S0219622019300039 Lama, S., Pradhan, S., & Shrestha, A. (2020). Exploration and implication of factors affecting e-tourism adoption in developing countries: A case of nepal. Information Technology and Tourism, 22(1), 5-32. doi:10.1007/s40558-019-00163-0 Liberato, P., Liberato, D., Abreu, A., Alén-González, E., & Rocha, Á. (2018). Generation Y: The competitiveness of the tourism sector based on digital technology doi:10.1007/978-3-319-74980-8_21 Retrieved from www.scopus.com Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. -., & Indragandhi, V. (2018). A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Systems, 83, 653-673. doi:10.1016/j.future.2017.08.060 Malik, R. (2021). Novel roadside unit positioning framework in the context of the vehicle-to-infrastructure communication system based on AHP—Entropy for weighting and borda—VIKOR for uniform ranking. Int J Inf Technol Decis Mak, , 1-34. Retrieved from www.scopus.com Meehan, K., Lunney, T., Curran, K., & McCaughey, A. (2013). Context-aware intelligent recommendation system for tourism. Paper presented at the 2013 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2013, 328-331. doi:10.1109/PerComW.2013.6529508 Retrieved from www.scopus.com Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017). A big data analytics method for tourist behaviour analysis. Information and Management, 54(6), 771-785. doi:10.1016/j.im.2016.11.011 Mohammed, K. I., Jaafar, J., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., Abdulkareem, K. H., . . . Alamoodi, A. H. (2020). A uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method. IEEE Access, 8, 91521-91530. doi:10.1109/ACCESS.2020.2994746 Mohammed, K. I., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., . . . Hashim, M. (2019). Real-time remote-health monitoring systems: A review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. Journal of Medical Systems, 43(7) doi:10.1007/s10916-019-1362-x Mohammed, R. T. (0000). Determining importance of many-objective optimisation competitive algorithms evaluation criteria based on a novel fuzzy-weighted zero-inconsistency method. Int J Inf Technol Decis Mak, , 1-47. Retrieved from www.scopus.com Mohammed, T. J., Albahri, A. S., Zaidan, A. A., Albahri, O. S., Al-Obaidi, J. R., Zaidan, B. B., . . . Hadi, S. M. (2021). Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. Applied Intelligence, 51(5), 2956-2987. doi:10.1007/s10489-020-02169-2 Napi, N. M., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., & Albahri, A. S. (2019). Medical emergency triage and patient prioritisation in a telemedicine environment: A systematic review. Health and Technology, 9(5), 679-700. doi:10.1007/s12553-019-00357-w Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507-520. doi:10.1016/j.eswa.2017.09.058 Nogueira, T. P., Braga, R. B., de Oliveira, C. T., & Martin, H. (2018). FrameSTEP: A framework for annotating semantic trajectories based on episodes. Expert Systems with Applications, 92, 533-545. doi:10.1016/j.eswa.2017.10.004 Osborn, W., & Hinze, A. (2014). TIP-tree: A spatial index for traversing locations in context-aware mobile access to digital libraries. Pervasive and Mobile Computing, 15, 26-47. doi:10.1016/j.pmcj.2013.12.002 Ospina, E., Moreno, F., & Uribe, I. A. (2015). Using criteria reconstruction for low-sampling trajectories as a tool for analytics. Paper presented at the Procedia Computer Science, , 51(1) 366-373. doi:10.1016/j.procs.2015.05.256 Retrieved from www.scopus.com Pamucar, D. (2020). Normalized weighted geometric dombi bonferroni mean operator with interval grey numbers: Application in multicriteria decision making. Reports in Mechanical Engineering, 1(1), 44-52. doi:10.31181/rme200101044p Pamučar, D., & Božanić, D. (2019). Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model. Operational Research in Engineering Sciences: Theory and Applications, 2(2), 55-71. doi:10.31181/oresta1902039p Pamučar, D., & Janković, A. (2020). The application of the hybrid interval rough weighted power heronian operator in multicriteria decision-making. Operational Research in Engineering Sciences: Theory and Applications, 3(2), 54-73. doi:10.31181/oresta2003049p Qian, Y., Zhang, Y., Ma, X., Yu, H., & Peng, L. (2019). EARS: Emotion-aware recommender system based on hybrid information fusion. Information Fusion, 46, 141-146. doi:10.1016/j.inffus.2018.06.004 Rodríguez-Hernández, M. D. C., & Ilarri, S. (2016). Pull-based recommendations in mobile environments. Computer Standards and Interfaces, 44, 185-204. doi:10.1016/j.csi.2015.08.002 Rongrong, Y. (2017). A mobile smart tourism and marketing system design for harbin. Paper presented at the Proceedings - 2017 International Conference on Robots and Intelligent System, ICRIS 2017, 12-14. doi:10.1109/ICRIS.2017.11 Retrieved from www.scopus.com Salih, M. M., Zaidan, B. B., & Zaidan, A. A. (2020). Fuzzy decision by opinion score method. Applied Soft Computing Journal, 96 doi:10.1016/j.asoc.2020.106595 Shin, D. -., Shin, Y. -., Choo, H., & Beom, K. (2011). Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior, 27(6), 2207-2214. doi:10.1016/j.chb.2011.06.017 Tariq, I. (2018). MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Computing and Applications, 32, 1-15. Retrieved from www.scopus.com Tsai, C. -., & Lai, B. -. (2015). A location-item-time sequential pattern mining algorithm for route recommendation. Knowledge-Based Systems, 73, 97-110. doi:10.1016/j.knosys.2014.09.012 Vdovenko, A. S., Marchenkov, S. A., & Korzun, D. G. (2015). Enhancing the smartroom system with e-tourism services. Paper presented at the Conference of Open Innovation Association, FRUCT, , 2015-June(June) 237-246. doi:10.1109/FRUCT.2015.7117999 Retrieved from www.scopus.com Wang, X., Li, X. R., Zhen, F., & Zhang, J. (2016). How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tourism Management, 54, 309-320. doi:10.1016/j.tourman.2015.12.003 Want, R., Schilit, B. N., & Jenson, S. (2015). Enabling the internet of things. Computer, 48(1), 28-35. doi:10.1109/MC.2015.12 Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 1339-1351. doi:10.1016/j.eswa.2016.09.040 Yeoman, I. (2012). 2050 - Tomorrow’s tourism (aspects of tourism, 55). 2050 - tomorrow's tourism (pp. 1-538) Retrieved from www.scopus.com Zhu, L., Xu, C., Guan, J., & Zhang, H. (2017). SEM-PPA: A semantical pattern and preference-aware service mining method for personalized point of interest recommendation. Journal of Network and Computer Applications, 82, 35-46. doi:10.1016/j.jnca.2016.12.033 Zughoul, O., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alazab, M., Amomeni, U., . . . Amomeni, B. (2021). Novel triplex procedure for ranking the ability of software engineering students based on two levels of AHP and group TOPSIS techniques. International Journal of Information Technology and Decision Making, 20(1), 67-135. doi:10.1142/S021962202050042X |
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