{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:59:01Z","timestamp":1772301541956,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, have created high expectations for industrial, commercial, and social value. Second, the emerging and growing concern for creating ethical and trusted AI systems, including compliance with regulatory principles to ensure transparency and trust. These two threads have created a kind of \u201cperfect storm\u201d of research activity, all motivated to create and deliver any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science and which provides a basis for the development of a framework for transparent XAI. We identify four foundational components, including the requirements for (1) explicit explanation knowledge representation, (2) delivery of alternative explanations, (3) adjusting explanations based on knowledge of the explainee, and (4) exploiting the advantage of interactive explanation. With those four components in mind, we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a basic history of XAI ideas, and then synthesize those ideas into a simple framework that can guide the design of AI systems that require XAI.<\/jats:p>","DOI":"10.3390\/make3040045","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T02:43:31Z","timestamp":1637289811000},"page":"900-921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence"],"prefix":"10.3390","volume":"3","author":[{"given":"Mi-Young","family":"Kim","sequence":"first","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"},{"name":"Department of Science, Augustana Faculty, University of Alberta, Camrose, AB T4V 2R3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-4656","authenticated-orcid":false,"given":"Shahin","family":"Atakishiyev","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"given":"Housam Khalifa Bashier","family":"Babiker","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6127-8220","authenticated-orcid":false,"given":"Nawshad","family":"Farruque","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0739-2946","authenticated-orcid":false,"given":"Randy","family":"Goebel","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0060-5988","authenticated-orcid":false,"given":"Osmar R.","family":"Za\u00efane","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0540-7531","authenticated-orcid":false,"given":"Mohammad-Hossein","family":"Motallebi","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2982-3401","authenticated-orcid":false,"given":"Juliano","family":"Rabelo","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"given":"Talat","family":"Syed","sequence":"additional","affiliation":[{"name":"XAI Lab, Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"given":"Hengshuai","family":"Yao","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Edmonton, AB T6G 2E8, Canada"}]},{"given":"Peter","family":"Chun","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2E8, Canada"},{"name":"Huawei Technologies Canada, Markham, ON L3R 5A4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"ref_1","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning. (See Especially Section 2, Chapter 9, The Limitations of Deep Learning)."},{"key":"ref_2","unstructured":"Rudin, C. (2018, January 7). Please stop explaining black box models for high stakes decisions. Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018), Workshop on Critiquing and Correcting Trends in Machine Learning, Montreal, QC, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Mallya, A., Tyree, S., Frosio, I., and Kautz, J. (2019). Importance Estimation for Neural Network Pruning. arXiv.","DOI":"10.1109\/CVPR.2019.01152"},{"key":"ref_4","unstructured":"Cohen, N. (2018, March 02). Can Increasing Depth Serve to Accelerate Optimization? 2018. BLOG: \u201cOff the Convex Path\u201d. Available online: https:\/\/www.offconvex.org\/2018\/03\/02\/acceleration-overparameterization\/."},{"key":"ref_5","unstructured":"Woodward, J. (2003). Scientific explanation. Stanford Encyclopedia of Philosophy, Stanford University. Available online: https:\/\/plato.stanford.edu\/entries\/scientific-explanation\/."},{"key":"ref_6","unstructured":"Pearl, J., and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect, Basic Books."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1145\/360018.360022","article-title":"Computer science as empirical inquiry: Symbols and search","volume":"19","author":"Newell","year":"1976","journal-title":"Commun. ACM"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bashier, H.K., Kim, M.Y., and Goebel, R. (2021, January 21). DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, online.","DOI":"10.18653\/v1\/2021.eacl-main.263"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","article-title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","volume":"267","author":"Miller","year":"2019","journal-title":"Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3233\/AAC-160001","article-title":"Combining explanation and argumentation in dialogue","volume":"7","author":"Bex","year":"2016","journal-title":"Argum. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Amarasinghe, K., Kenney, K., and Manic, M. (2018, January 4\u20136). Toward explainable deep neural network based anomaly detection. Proceedings of the 2018 11th International Conference on Human System Interaction (HSI), Gdansk, Poland.","DOI":"10.1109\/HSI.2018.8430788"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nascita, A., Montieri, A., Aceto, G., Ciuonzo, D., Persico, V., and Pescap\u00e9, A. (2021). XAI meets mobile traffic classification: Understanding and improving multimodal deep learning architectures. IEEE Trans. Netw. Serv. Manag.","DOI":"10.1109\/TNSM.2021.3098157"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Meng, Z., Wang, M., Bai, J., Xu, M., Mao, H., and Hu, H. (2020, January 10\u201314). Interpreting deep learning-based networking systems. Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication, online.","DOI":"10.1145\/3387514.3405859"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Morichetta, A., Casas, P., and Mellia, M. (2019, January 9). EXPLAIN-IT: Towards explainable AI for unsupervised network traffic analysis. Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks, Orlando, FL, USA.","DOI":"10.1145\/3359992.3366639"},{"key":"ref_15","unstructured":"Koh, P.W., and Liang, P. (2017). Understanding Black-box Predictions via Influence Functions. arXiv."},{"key":"ref_16","unstructured":"Vaughan, J., Sudjianto, A., Brahimi, E., Chen, J., and Nair, V.N. (2018). Explainable Neural Networks based on Additive Index Models. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., and Kagal, L. (2018, January 1). Explaining Explanations: An Overview of Interpretability of Machine Learning. Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy.","DOI":"10.1109\/DSAA.2018.00018"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mendelson, E. (2015). Introduction to Mathematical Logic, CRC Press. [6th ed.].","DOI":"10.1201\/b18519"},{"key":"ref_20","unstructured":"d\u2019Avila Garcez, A., and Lamb, L.C. (2020). Neurosymbolic AI: The 3rd Wave. arXiv."},{"key":"ref_21","unstructured":"Babiker, H., and Goebel, R. (2017, January 4\u20139). An Introduction to Deep Visual Explanation. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_22","unstructured":"Lent, R.C. (2012). Overcoming Textbook Fatigue: 21st Century Tools to Revitalize Teaching and Learning, Association for Supervision and Curriculum Development."},{"key":"ref_23","unstructured":"British Broadcasting Corporation (2019, November 11). Richard Feynman: Magnets and Why Questions. Available online: https:\/\/www.youtube.com\/watch?v=Dp4dpeJVDxs."},{"key":"ref_24","unstructured":"L\u00e9cu\u00e9, F., and Pommellet, T. (2019, January 26\u201330). Feeding Machine Learning with Knowledge Graphs for Explainable Object Detection. Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1613\/jair.5714","article-title":"Learning Explanatory Rules from Noisy Data","volume":"61","author":"Evans","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_28","unstructured":"Goodfellow, I.J., Shlens, J., and Szegedy, C. (2014). Explaining and Harnessing Adversarial Examples. arXiv."},{"key":"ref_29","unstructured":"Povolny, S., and Trivedi, S. (2021, November 15). Model Hacking ADAS to Pave Safer Roads for Autonomous Vehicles. Available online: https:\/\/www.mcafee.com\/blogs\/other-blogs\/mcafee-labs\/model-hacking-adas-to-pave-safer-roads-for-autonomous-vehicles\/."},{"key":"ref_30","unstructured":"Adiwardana, D., Luong, M.T., So, D.R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., and Lu, Y. (2020). Towards a Human-like Open-Domain Chatbot. arXiv."},{"key":"ref_31","unstructured":"Dickson, B. (2020, April 14). Artificial Intelligence: Does Another Huge Language Model Prove Anything?. Available online: https:\/\/bdtechtalks.com\/2020\/02\/03\/google-meena-chatbot-ai-language-model\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., and Zhu, W. (2002, January 6\u201312). BLEU: A method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting on Association for Computational Linguistics (ACL), Philadelphia, PA, USA.","DOI":"10.3115\/1073083.1073135"},{"key":"ref_33","unstructured":"Lin, C.Y. (2004). ROUGE: A Package for Automatic Evaluation of Summaries, Text Summarization Branches Out; Association for Computational Linguistics."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, C.W., Lowe, R., Serban, I.V., Noseworthy, M., Charlin, L., and Pineau, J. (2016, January 1\u20135). How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1230"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Spence, R. (2014). Information Visualization, Springer. [3rd ed.].","DOI":"10.1007\/978-3-319-07341-5"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/TVCG.2011.279","article-title":"Empirical Studies in Information Visualization: Seven Scenarios","volume":"18","author":"Lam","year":"2012","journal-title":"IEEE Trans. Graph. Vis. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Goebel, R., Shi, W., and Tanaka, Y. (2013, January 16\u201318). The role of direct manipulation of visualizations in the development and use of multi-level knowledge models. Proceedings of the 17th International Conference on Information Visualisation, IV\u201913, London, UK.","DOI":"10.1109\/IV.2013.95"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/S0020-7373(86)80004-9","article-title":"A comparative analysis of methods for expert systems","volume":"24","author":"Ramsey","year":"1986","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_39","unstructured":"Lucas, P., and Van Der Gaag, L. (1991). Principles of Expert Systems, Addison-Wesley."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"161","DOI":"10.5840\/monist18911211","article-title":"The architecture of theories","volume":"1","author":"Peirce","year":"1891","journal-title":"Monist"},{"key":"ref_41","unstructured":"Pople, H. (1973, January 20\u201323). On the mechanization of abductive logic. Proceedings of the IJCAI\u201973: Proceedings of the 3rd International Joint Conference on Artificial Intelligence, Stanford, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Poole, D., Goebel, R., and Aleliunas, R. (1987). Theorist: A Logical Reasoning System for Defaults and Diagnosis. Knowl. Front. Symb. Comput. (Artif. Intell.), 331\u2013352.","DOI":"10.1007\/978-1-4612-4792-0_13"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Muggleton, S. (1991). Inductive Logic Programming. New Generation Computing, Springer.","DOI":"10.1007\/BF03037089"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1017\/S0140525X00057046","article-title":"Explanatory coherence","volume":"12","author":"Thagard","year":"1989","journal-title":"Behav. Brain Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1111\/j.1471-6712.1997.tb00455.x","article-title":"Abduction\u2014A way to deeper understanding of the world of caring","volume":"11","author":"Eriksson","year":"1997","journal-title":"Scand. J. Caring Sci."},{"key":"ref_46","unstructured":"Jokhio, I., and Chalmers, I. (2021, October 02). Using Your Logical Powers: Abductive Reasoning for Business Success. Available online: https:\/\/uxpamagazine.org\/using-your-logical-powers\/."},{"key":"ref_47","unstructured":"Falcon, A., and Aristotle on Causality (2006, January 11). Stanford Encyclopedia of Philosophy. Available online: https:\/\/plato.stanford.edu\/entries\/aristotle-causality\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2307\/2017635","article-title":"The function of general laws in history","volume":"39","author":"Hempel","year":"1942","journal-title":"J. Philos."},{"key":"ref_49","first-page":"173","article-title":"The Theoretician\u2019s Dilemma: A Study in the Logic of Theory Construction","volume":"2","author":"Hempel","year":"1958","journal-title":"Minn. Stud. Philos. Sci."},{"key":"ref_50","unstructured":"Hempel, C.G. (1965). Aspects of Scientific Explanation, Free Press."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1086\/286983","article-title":"Studies in the Logic of Explanation","volume":"15","author":"Hempel","year":"1948","journal-title":"Philos. Sci."},{"key":"ref_52","unstructured":"McGrew, T., Alspector-Kelly, M., and Allhoff, F. (2009). Philosophy of Science: An Historical Anthology, John Wiley & Sons."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bunzl, M. (1993). The Context of Explanation, Springer Science and Business Media. Boston Studies in the Philosophy and History of Science.","DOI":"10.1007\/978-94-011-1735-7"},{"key":"ref_54","unstructured":"Janssen, T. (2020, February 24). Montague Semantics. Available online: https:\/\/plato.stanford.edu\/entries\/montague-semantics\/."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lei, T., Barzilay, R., and Jaakkola, T. (2016, January 1\u20135). Rationalizing Neural Predictions. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1011"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 12\u201317). Hierarchical attention networks for document classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref_57","unstructured":"Serrano, S., and Smith, N.A. (August, January 28). Is Attention Interpretable?. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_58","unstructured":"Jain, S., and Wallace, B.C. (2019). Attention is not explanation. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). Why should i trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_61","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_62","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F. (2018). Local rule-based explanations of black box decision systems. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hendricks, L.A., Hu, R., Darrell, T., and Akata, Z. (2018, January 8\u201314). Grounding visual explanations. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_17"},{"key":"ref_64","unstructured":"Bastings, J., Aziz, W., and Titov, I. (28\u20132, January 28). Interpretable Neural Predictions with Differentiable Binary Variables. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_66","unstructured":"Kasirzadeh, A. (2019). Mathematical Decisions & Non-causal Elements of Explainable AI. arXiv."},{"key":"ref_67","unstructured":"Madumal, P., Miller, T., Vetere, F., and Sonenberg, L. (2018). Towards a Grounded Dialog Model for Explainable Artificial Intelligence. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.cognition.2016.10.024","article-title":"Normality: Part descriptive, Part prescriptive","volume":"167","author":"Bear","year":"2017","journal-title":"Cognition"},{"key":"ref_69","unstructured":"Gunning, D. (2016, August 11). Explainable Artificial Intelligence. November 2017. Available online: https:\/\/www.darpa.mil\/attachments\/XAIIndustryDay_Final.pptx."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/3\/4\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:58Z","timestamp":1760167918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/3\/4\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"references-count":69,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["make3040045"],"URL":"https:\/\/doi.org\/10.3390\/make3040045","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,18]]}}}