Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

A systematic review of digital transformation technologies in museum exhibition

Published: 18 November 2024 Publication History

Abstract

Museum exhibitions, both temporary and permanent, form an essential link between a society and its cultural, historical, and artistic heritage sites. Curating artifacts and thematic displays in museum exhibitions can promote dialogue, foster cultural appreciation, and contribute to heritage preservation. The traditional way of holding museum exhibitions, heavily reliant on the expertise of designers and curatorial staff, makes them a labor-intensive process, from conceptualization to visitor engagement analysis. This review systematically compiles and examines how the application of digital transformation technologies (DTTs) has revolutionized museum exhibitions and augmented their future potential. DTTs such as artificial intelligence, immersive technologies, additive manufacturing, the Internet of Things, and cloud computing can help create engaging designs, improve accessibility and inclusivity, enhance educational potential, and allow for sophisticated visitor experience data collection and analyses, improving exhibit management. However, despite multiple specialized studies on DTTs and their roles in museum exhibitions, the connections between technology and application scenarios remain underexplored. By addressing this gap, this study is expected to inform and inspire practitioners in the museum and heritage sectors and present new research avenues for scholars.

Graphical abstract

Display Omitted

Highlights

Comprehensive analysis of digital transformation technologies (DTTs) in museums.
DTTs improve museum exhibitions: design, accessibility, education, and management.
DTTs innovation enhances museum visitor experiences.
Uncovers avenues for future research on DTTs integration in museum exhibitions.
Bridges academia and the cultural heritage industry with DTTs in museums.

References

[1]
Abdel-Hamid O., Mohamed A.-r., Jiang H., Deng L., Penn G., Yu D., Convolutional neural networks for speech recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing 22 (10) (2014) 1533–1545.
[2]
Adamopoulou E., Moussiades L., Chatbots: History, technology, and applications, Machine Learning with Applications 2 (2020).
[3]
Agostino D., Arnaboldi M., Lampis A., Italian state museums during the COVID-19 crisis: from onsite closure to online openness, Museum Management and Curatorship 35 (4) (2020) 362–372.
[4]
Al Fararni K., Nafis F., Aghoutane B., Yahyaouy A., Riffi J., Sabri A., Hybrid recommender system for tourism based on big data and AI: A conceptual framework, Big Data Mining and Analytics 4 (1) (2021) 47–55.
[5]
Alletto S., Cucchiara R., Del Fiore G., Mainetti L., Mighali V., Patrono L., et al., An indoor location-aware system for an IoT-based smart museum, IEEE Internet of Things Journal 3 (2) (2015) 244–253.
[6]
Alsuhly G., Khattab A., An IoT monitoring and control platform for museum content conservation, in: 2018 international conference on computer and applications, IEEE, 2018, pp. 196–201.
[7]
Amakawa J., Westin J., New philadelphia: using augmented reality to interpret slavery and reconstruction era historical sites, International Journal of Heritage Studies 24 (3) (2018) 315–331.
[8]
Andre L., Durksen T., Volman M.L., Museums as avenues of learning for children: A decade of research, Learning Environments Research 20 (2017) 47–76.
[9]
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., et al. (2023). Palm 2: technical report, arXiv preprint arXiv:2305.10403.
[10]
Aoun J., Robot-proof: higher education in the age of artificial intelligence, MIT Press, 2017.
[11]
Armbrust M., Fox A., Griffith R., Joseph A.D., Katz R., Konwinski A., et al., A view of cloud computing, Communications of the ACM 53 (4) (2010) 50–58.
[12]
Atik M.E., Duran Z., Yanalak M., Seker D.Z., Ak A., 3D modeling of historical measurement instruments using photogrammetric and laser scanning techniques, Digital Applications in Archaeology and Cultural Heritage 30 (2023).
[13]
Baker E., Bakar J.A., Zulkifli A., A conceptual model of mobile augmented reality for hearing impaired museum visitors’ engagement, 2020.
[14]
Barlow H.B., Unsupervised learning, Neural Computation 1 (3) (1989) 295–311.
[15]
Bertacchini E., Morando F., The future of museums in the digital age: New models for access to and use of digital collections, International Journal of Arts Management 15 (2) (2013) 60–72.
[16]
Bile A., Tari H., Grinde A., Frasca F., Siani A.M., Fazio E., Novel model based on artificial neural networks to predict short-term temperature evolution in museum environment, Sensors 22 (2) (2022) 615.
[17]
Billinghurst M., Clark A., Lee G., et al., A survey of augmented reality, Foundations and Trends® in Human–Computer Interaction 8 (2–3) (2015) 73–272.
[18]
Bird S., Klein E., Loper E., Natural language processing with Python: analyzing text with the natural language toolkit, O’Reilly Media, Inc., 2009.
[19]
Booster M., Museum innovation barometer 2021, 2021, pp. 11–14. URL https://museumbooster.com/wp-content/uploads/2021/08/Museum-Innovation-Barometer-2021.pdf.
[20]
Borràs J., Moreno A., Valls A., Intelligent tourism recommender systems: A survey, Expert Systems with Applications 41 (16) (2014) 7370–7389.
[21]
Boyer D.M., Gunnell G.F., Kaufman S., McGeary T.M., Morphosource: archiving and sharing 3-D digital specimen data, The Paleontological Society Papers 22 (2016) 157–181.
[22]
Bozzelli G., Raia A., Ricciardi S., De Nino M., Barile N., Perrella M., et al., An integrated VR/AR framework for user-centric interactive experience of cultural heritage: The ArkaeVision project, Digital Applications in Archaeology and Cultural Heritage 15 (2019).
[23]
British Museum G., Samsung digital discovery centre, 2021, https://www.britishmuseum.org/learn/schools/samsung-digital-discovery-centre.
[24]
Bruno F., Bruno S., De Sensi G., Luchi M.-L., Mancuso S., Muzzupappa M., From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition, Journal of Cultural Heritage 11 (1) (2010) 42–49.
[25]
Budge K., Burness A., Museum objects and instagram: agency and communication in digital engagement, Continuum 32 (2) (2018) 137–150.
[26]
Bugeja M., Grech E.M., Using technology and gamification as a means of enhancing users’ experience at cultural heritage sites, Rediscovering Heritage Through Technology: A Collection of Innovative Research Case Studies that are Reworking the Way We Experience Heritage (2020) 69–89.
[27]
Burke V., Jørgensen D., Jørgensen F.A., Museums at home: Digital initiatives in response to COVID-19, Norsk museumstidsskrift 6 (2) (2020) 117–123.
[28]
Carmigniani J., Furht B., Augmented reality: an overview, Handbook of augmented reality (2011) 3–46.
[29]
Carneiro O.S., Silva A., Gomes R., Fused deposition modeling with polypropylene, Materials & Design 83 (2015) 768–776.
[30]
Carrozzino M., Bergamasco M., Beyond virtual museums: Experiencing immersive virtual reality in real museums, Journal of Cultural Heritage 11 (4) (2010) 452–458.
[31]
Caruana R., Niculescu-Mizil A., An empirical comparison of supervised learning algorithms, in: Proceedings of the 23rd international conference on machine learning, 2006, pp. 161–168.
[32]
Cavazos Quero L., Iranzo Bartolomé J., Cho J., Accessible visual artworks for blind and visually impaired people: comparing a multimodal approach with tactile graphics, Electronics 10 (3) (2021) 297.
[33]
Centorrino P., Corbetta A., Cristiani E., Onofri E., Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization, Journal of Computer Science 53 (2021).
[34]
Cepeda-Pacheco J.C., Domingo M.C., Deep learning and internet of things for tourist attraction recommendations in smart cities, Neural Computing and Applications 34 (10) (2022) 7691–7709.
[35]
Cesário V., Nisi V., Designing with teenagers: A teenage perspective on enhancing mobile museum experiences, International Journal of Child-Computer Interaction 33 (2022).
[36]
Chanhom W., Anutariya C., TOMS: A linked open data system for collaboration and distribution of cultural heritage artifact collections of national museums in Thailand, New Generation Computing 37 (2019) 479–498.
[37]
Checa D., Bustillo A., A review of immersive virtual reality serious games to enhance learning and training, Multimedia Tools and Applications 79 (9) (2020) 5501–5527.
[38]
Cheng J., Bernstein M.S., Flock: Hybrid crowd-machine learning classifiers, in: Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, 2015, pp. 600–611.
[39]
[40]
Collobert R., Weston J., Bottou L., Karlen M., Kavukcuoglu K., Kuksa P., Natural language processing (almost) from scratch, Journal of Machine Learning Research 12 (ARTICLE) (2011) 2493–2537.
[41]
Comes R., Neamţu C., Buna Z.L., Bodi D., Popescu D., Tompa V., et al., Enhancing accessibility to cultural heritage through digital content and virtual reality: A case study of the sarmizegetusa regia UNESCO site, Journal of Ancient History and Archaeology 7 (3) (2020).
[42]
Cuomo S., De Michele P., Galletti A., Piccialli F., A cultural heritage case study of visitor experiences shared on a social network, in: 2015 10th international conference on p2P, parallel, grid, cloud and internet computing, 3PGCIC, IEEE, 2015, pp. 539–544.
[43]
Da Xu L., He W., Li S., Internet of things in industries: A survey, IEEE Transactions on industrial informatics 10 (4) (2014) 2233–2243.
[44]
Darda K., Carre M., Cross E., Value attributed to text-based archives generated by artificial intelligence, Royal Society Open Science 10 (2) (2023).
[45]
Darzentas D., Cameron H., Wagner H., Craigon P., Bodiaj E., Spence J., et al., Data-inspired co-design for museum and gallery visitor experiences, AI EDAM 36 (2022).
[46]
De Freitas G., Gelaim T., De Mello R., Silveira R., Perception policies for intelligent virtual agents, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 8 (2) (2019) 87–95.
[47]
DeHass M.K.C., Taitt A., 3D technology in collaborative heritage preservation, Museum Anthropology Review 12 (2) (2018) 120–152.
[48]
Di Franco P.D.G., Camporesi C., Galeazzi F., Kallmann M., 3D printing and immersive visualization for improved perception of ancient artifacts, Presence: Teleoperators and Virtual Environments 24 (3) (2015) 243–264.
[49]
Dillon T., Wu C., Chang E., Cloud computing: issues and challenges, in: 2010 24th IEEE international conference on advanced information networking and applications, Ieee, 2010, pp. 27–33.
[50]
Dionisio M., Nisi V., Leveraging transmedia storytelling to engage tourists in the understanding of the destination’s local heritage, Multimedia Tools and Applications 80 (26–27) (2021) 34813–34841.
[51]
Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., et al., An image is worth 16x16 words: Transformers for image recognition at scale, 2020, arXiv preprint arXiv:2010.11929.
[52]
Duguleană M., Briciu V.-A., Duduman I.-A., Machidon O.M., A virtual assistant for natural interactions in museums, Sustainability 12 (17) (2020) 6958.
[53]
Emerson, A., Henderson, N., Rowe, J., Min, W., Lee, S., Minogue, J., et al. (2020). Early prediction of visitor engagement in science museums with multimodal learning analytics. In Proceedings of the 2020 international conference on multimodal interaction (pp. 107–116).
[54]
Erkan G., Radev D.R., Lexrank: Graph-based lexical centrality as salience in text summarization, Journal of Artificial Intelligence Research 22 (2004) 457–479.
[55]
Ferrato A., Limongelli C., Mezzini M., Sansonetti G., Using deep learning for collecting data about museum visitor behavior, Applied Sciences 12 (2) (2022) 533.
[56]
Forsyth D.A., Ponce J., Computer vision: a modern approach, prentice hall professional technical reference, 2002.
[57]
French, A., & Villaespesa, E. (2019). AI, visitor experience, and museum operations: a closer look at the possible. In Humanizing the digital: unproceedings from the MCN 2018 conference (pp. 101–113).
[58]
Friel R.J., Harris R.A., Ultrasonic additive manufacturing–a hybrid production process for novel functional products, Procedia Cirp 6 (2013) 35–40.
[59]
Gaia G., Boiano S., Borda A., Engaging museum visitors with AI: The case of chatbots, Museums and Digital Culture: New Perspectives and Research (2019) 309–329.
[60]
García-Molina D.F., López-Lago S., Hidalgo-Fernandez R.E., Triviño-Tarradas P., Digitalization and 3D documentation techniques applied to two pieces of visigothic sculptural heritage in merida through structured light scanning, Journal on Computing and Cultural Heritage (JOCCH) 14 (4) (2021) 1–19.
[61]
Gea M., Alaman X., Rodríguez P., Martinez V., Towards smart & inclusive society: building 3D immersive museum by children with cognitive disabilities, in: EDULEARN16 proceedings, IATED, 2016, pp. 5260–5268.
[62]
Germak C., Lupetti M.L., Giuliano L., Ng M.E.K., Robots and cultural heritage: New museum experiences, Journal of Science and Technology of the Arts 7 (2) (2015) 47–57.
[63]
Ghahramani Z., Probabilistic machine learning and artificial intelligence, Nature 521 (7553) (2015) 452–459.
[64]
Giannini T., Bowen J.P., Museums and digital culture: From reality to digitality in the age of COVID-19, Heritage 5 (1) (2022) 192–214.
[65]
Gibson I., Rosen D., Stucker B., Khorasani M., Gibson I., Rosen D., et al., Binder jetting, Additive manufacturing technologies (2021) 237–252.
[66]
Gibson I., Rosen D., Stucker B., Khorasani M., Gibson I., Rosen D., et al., Material jetting, Additive Manufacturing Technologies (2021) 203–235.
[67]
Gibson I., Rosen D., Stucker B., Khorasani M., Rosen D., Stucker B., et al., Additive manufacturing technologies, Springer, 2021.
[68]
Glover F., Future paths for integer programming and links to artificial intelligence, Computers & Operations Research 13 (5) (1986) 533–549.
[69]
Gonzalez A.J., Hollister J.R., DeMara R.F., Leigh J., Lanman B., Lee S.-Y., et al., AI in informal science education: Bringing turing back to life to perform the turing test, International Journal of Artificial Intelligence in Education 27 (2017) 353–384.
[70]
Goodfellow I., Bengio Y., Courville A., Deep learning, MIT Press, 2016.
[71]
Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., et al., Generative adversarial networks, Communications of the ACM 63 (11) (2020) 139–144.
[72]
Gu D., Shi X., Poprawe R., Bourell D.L., Setchi R., Zhu J., Material-structure-performance integrated laser-metal additive manufacturing, Science 372 (6545) (2021) eabg1487.
[73]
Guarino M., Di Palma M.A., Menini T., Gallo M., Digital transformation of cultural institutions: a statistical analysis of Italian and Campania GLAMs, Quality & Quantity 54 (2020) 1445–1464.
[74]
Gülcan O., Günaydın K., Tamer A., The state of the art of material jetting—a critical review, Polymers 13 (16) (2021) 2829.
[75]
Guo X., Zheng X., Yang Y., Yang X., Yi Y., Mechanical behavior of TPMS-based scaffolds: a comparison between minimal surfaces and their lattice structures, SN Applied Sciences 1 (10) (2019) 1145.
[76]
Hakulinen J., Keskinen T., Mäkelä V., Saarinen S., Turunen M., Omnidirectional video in museums–authentic, immersive and entertaining, in: Advances in computer entertainment technology: 14th international conference, ACE 2017, London, UK, December 14-16, 2017, proceedings 14, Springer, 2018, pp. 567–587.
[77]
Hammady R., Ma M., Strathern C., Mohamad M., Design and development of a spatial mixed reality touring guide to the Egyptian museum, Multimedia Tools and Applications 79 (2020) 3465–3494.
[78]
Hashemi S.H., Kamps J., Exploiting behavioral user models for point of interest recommendation in smart museums, New Review of Hypermedia and Multimedia 24 (3) (2018) 228–261.
[79]
Hastie T., Friedman J., Tibshirani R., Hastie T., Friedman J., Tibshirani R., Unsupervised learning, The elements of statistical learning: Data mining, inference, and prediction (2001) 437–508.
[80]
Haugeland J., Artificial intelligence: The very idea, MIT Press, 1989.
[81]
He Z., Wu L., Li X.R., When art meets tech: The role of augmented reality in enhancing museum experiences and purchase intentions, Tourism Management 68 (2018) 127–139.
[82]
He D., Xia Y., Qin T., Wang L., Yu N., Liu T.-Y., et al., Dual learning for machine translation, Advances in Neural Information Processing Systems 29 (2016).
[83]
Hehr A., Norfolk M., A comprehensive review of ultrasonic additive manufacturing, Rapid Prototyping Journal 26 (3) (2019) 445–458.
[84]
Hess M., Petrovic V., Meyer D., Rissolo D., Kuester F., Fusion of multimodal three-dimensional data for comprehensive digital documentation of cultural heritage sites, in: 2015 digital heritage, 2, IEEE, 2015, pp. 595–602.
[85]
Hirschberg J., Manning C.D., Advances in natural language processing, Science 349 (6245) (2015) 261–266.
[86]
Holler J., Tsiatsis V., Mulligan C., Karnouskos S., Avesand S., Boyle D., Internet of things, Academic Press, 2014.
[87]
Horton J., Xiong J.A., A comparison of three emerging online government 3D printing resources: NASA 3D resources, smithsonian X3D, and the NIH 3D print exchange, The Charleston Advisor 18 (2) (2016) 5–10.
[89]
Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. 8, In Proceedings of the international AAAI conference on web and social media (1), (pp. 216–225).
[90]
International Council of Museums C., Museums, museum professionals and COVID-19, 2020, pp. 2–3. URL https://icom.museum/wp-content/uploads/2020/05/Report-Museums-and-COVID-19.pdf.
[91]
International Council of Museums C., ICOM report: museums, museum professionals and Covid-19: third survey, 2021, pp. 15–16. URL https://icom.museum/wp-content/uploads/2021/07/Museums-and-Covid-19_third-ICOM-report.pdf.
[92]
Isinkaye F.O., Folajimi Y.O., Ojokoh B.A., Recommendation systems: Principles, methods and evaluation, Egyptian informatics journal 16 (3) (2015) 261–273.
[93]
Jones, L., Nousir, A., Everrett, T., & Nabil, S. (2023). Libraries of Things: Understanding the Challenges of Sharing Tangible Collections and the Opportunities for HCI. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1–18).
[94]
Jordan M.I., Mitchell T.M., Machine learning: Trends, perspectives, and prospects, Science 349 (6245) (2015) 255–260.
[95]
Kaelbling L.P., Littman M.L., Moore A.W., Reinforcement learning: A survey, Journal of Artificial Intelligence Research 4 (1996) 237–285.
[96]
Kaplan A., Innovation in artificial intelligence: Illustrations in academia, apparel, and the arts, in: Oxford research encyclopedia of business and management, 2023.
[97]
Katz J.E., Halpern D., Can virtual museums motivate students? Toward a constructivist learning approach, Journal of Science Education and Technology 24 (2015) 776–788.
[98]
Keil J., Pujol L., Roussou M., Engelke T., Schmitt M., Bockholt U., et al., A digital look at physical museum exhibits: Designing personalized stories with handheld augmented reality in museums, in: 2013 digital heritage international congress (digitalHeritage), 2, IEEE, 2013, pp. 685–688.
[99]
Kennedy A.A., Thacker I., Nye B.D., Sinatra G.M., Swartout W., Lindsey E., Promoting interest, positive emotions, and knowledge using augmented reality in a museum setting, International Journal of Science Education, Part B 11 (3) (2021) 242–258.
[100]
Kersten T.P., Tschirschwitz F., Deggim S., Development of a virtual museum including a 4D presentation of building history in virtual reality, The international archives of the photogrammetry, remote sensing and spatial information sciences 42 (2017) 361–367.
[101]
Khan M.A., Israr S., S. Almogren A., Din I.U., Almogren A., Rodrigues J.J., Using augmented reality and deep learning to enhance taxila museum experience, Journal of Real-Time Image Processing 18 (2021) 321–332.
[102]
Kim H.J., Lee H.-K., Emotions and colors in a design archiving system: Applying AI technology for museums, Applied Sciences 12 (5) (2022) 2467.
[103]
King E., Smith M., Wilson P., Williams M., Digital responses of UK museum exhibitions to the COVID-19 crisis, march–june 2020, Curator: The Museum Journal 64 (3) (2021) 487–504.
[104]
Kingma D.P., Welling M., et al., An introduction to variational autoencoders, Foundations and Trends® in Machine Learning 12 (4) (2019) 307–392.
[105]
Kiourt C., Koutsoudis A., Pavlidis G., DynaMus: A fully dynamic 3D virtual museum framework, Journal of Cultural Heritage 22 (2016) 984–991.
[106]
Konev A., Khaydarova R., Lapaev M., Feng L., Hu L., Chen M., et al., CHPC: A complex semantic-based secured approach to heritage preservation and secure IoT-based museum processes, Computer Communications 148 (2019) 240–249.
[107]
Kotsiantis S., Zaharakis I., Pintelas P., et al., Supervised machine learning: A review of classification techniques, Emerging Artificial Intelligence Applications in Computer Engineering 160 (1) (2007) 3–24.
[108]
Kounavis C.D., Kasimati A.E., Zamani E.D., Enhancing the tourism experience through mobile augmented reality: Challenges and prospects, International Journal of Engineering Business Management 4 (2012) 10.
[109]
Kuflik T., Wecker A.J., Lanir J., Stock O., An integrative framework for extending the boundaries of the museum visit experience: linking the pre, during and post visit phases, Information Technology & Tourism 15 (2015) 17–47.
[110]
Kuzminsky S.C., Gardiner M.S., Three-dimensional laser scanning: potential uses for museum conservation and scientific research, Journal of Archaeological Science 39 (8) (2012) 2744–2751.
[111]
Kyriakou P., Hermon S., Can I touch this? Using natural interaction in a museum augmented reality system, Digital Applications in Archaeology and Cultural Heritage 12 (2019).
[112]
La Russa F.M., Santagati C., An AI-based DSS for preventive conservation of museum collections in historic buildings, Journal of Archaeological Science: Reports 35 (2021).
[113]
Lanir J., Kuflik T., Sheidin J., Yavin N., Leiderman K., Segal M., Visualizing museum visitors’ behavior: Where do they go and what do they do there?, Personal and Ubiquitous Computing 21 (2017) 313–326.
[114]
Lecoutre A., Negrevergne B., Yger F., Recognizing art style automatically in painting with deep learning, in: Asian conference on machine learning, PMLR, 2017, pp. 327–342.
[115]
LeCun Y., Bengio Y., Hinton G., Deep learning, Nature 521 (7553) (2015) 436–444.
[116]
Lécuyer A., Simulating haptic feedback using vision: A survey of research and applications of pseudo-haptic feedback, Presence: Teleoperators and Virtual Environments 18 (1) (2009) 39–53.
[117]
Lee J.-Y., An J., Chua C.K., Fundamentals and applications of 3D printing for novel materials, Applied materials today 7 (2017) 120–133.
[118]
Lee J., Lee H.-K., Jeong D., Lee J., Kim T., Lee J., Developing museum education content: AR blended learning, International Journal of Art & Design Education 40 (3) (2021) 473–491.
[119]
Lee S., Yun J., Lee S., Song Y., Song H., Will AI image synthesis technology help constructivist education at the online art museum?, in: CHI conference on human factors in computing systems extended abstracts, 2022, pp. 1–7.
[120]
Leue M.C., Jung T., tom Dieck D., Google glass augmented reality: Generic learning outcomes for art galleries, in: Information and communication technologies in tourism 2015: proceedings of the international conference in lugano, Switzerland, February 3-6, 2015, Springer, 2015, pp. 463–476.
[121]
Levinson J., Askeland J., Becker J., Dolson J., Held D., Kammel S., et al., Towards fully autonomous driving: Systems and algorithms, in: 2011 IEEE intelligent vehicles symposium, IEEE, 2011, pp. 163–168.
[122]
Lewandowski J.J., Seifi M., Metal additive manufacturing: a review of mechanical properties, Annual Review of Materials Research 46 (2016) 151–186.
[123]
Li S., Xu L.D., Zhao S., The internet of things: a survey, Information systems frontiers 17 (2015) 243–259.
[124]
Li J., Zheng X., Lu J.-L., Xanat V.M., Ochiai Y., Transformation of plants into polka dot arts: Kusama yayoi as an inspiration for deep learning, in: Universal access in human-computer interaction. novel design approaches and technologies: 16th international conference, UAHCI 2022, held as part of the 24th HCI international conference, HCII 2022, virtual event, June 26–July 1, 2022, proceedings, part i, Springer, 2022, pp. 270–280.
[125]
Liao H.-T., Zhao M., Sun S.-P., A literature review of museum and heritage on digitization, digitalization, and digital transformation, in: 6th international conference on humanities and social science research, Atlantis Press, 2020, pp. 473–476.
[126]
Lin C.-L., Chen S.-J., Lin R., Efficacy of virtual reality in painting art exhibitions appreciation, Applied Sciences 10 (9) (2020) 3012.
[127]
Liu Z., Wang M., Qi S., Yang C., Study on the anti-theft technology of museum cultural relics based on internet of things, IEEE Access 7 (2019) 111387–111395.
[128]
Lu X., Lin Z., Jin H., Yang J., Wang J.Z., Rapid: Rating pictorial aesthetics using deep learning, in: Proceedings of the 22nd ACM international conference on multimedia, 2014, pp. 457–466.
[129]
Mahmood T., Fulmer W., Mungoli N., Huang J., Lu A., Improving information sharing and collaborative analysis for remote geospatial visualization using mixed reality, in: 2019 IEEE international symposium on mixed and augmented reality, IEEE, 2019, pp. 236–247.
[130]
Manning C., Schutze H., Foundations of statistical natural language processing, MIT Press, 1999.
[131]
Markopoulos E., Ye C., Markopoulos P., Luimula M., Digital museum transformation strategy against the Covid-19 pandemic crisis, in: Advances in creativity, innovation, entrepreneurship and communication of design: proceedings of the AHFE 2021 virtual conferences on creativity, innovation and entrepreneurship, and human factors in communication of design, Springer, 2021, pp. 225–234.
[132]
Martella C., Miraglia A., Frost J., Cattani M., van Steen M., Visualizing, clustering, and predicting the behavior of museum visitors, Pervasive and Mobile Computing 38 (2017) 430–443.
[133]
Marty P.F., Jones K.B., Museum informatics: People, information, and technology in museums, Taylor & Francis, 2008.
[134]
Melchels F.P., Feijen J., Grijpma D.W., A review on stereolithography and its applications in biomedical engineering, Biomaterials 31 (24) (2010) 6121–6130.
[135]
Meliones A., Sampson D., Blind MuseumTourer: A system for self-guided tours in museums and blind indoor navigation, Technologies 6 (1) (2018) 4.
[136]
Meng Y., Chu M.Y., Chiu D.K., The impact of COVID-19 on museums in the digital era: practices and challenges in Hong Kong, Library Hi Tech 41 (1) (2023) 130–151.
[137]
Menna F., Rizzi A., Nocerino E., Remondino F., Gruen A., ∖High resolution 3D modeling of the behaim globe, The international archives of the photogrammetry, remote sensing and spatial information sciences 39 (2012) 115–120.
[138]
Merritt E., Artificial intelligence the rise of the intelligent machine, 2017, URL https://www.aam-us.org/2017/05/01/artificial-intelligence-the-rise-of-the-intelligent-machine/.
[139]
Milgram P., Kishino F., A taxonomy of mixed reality visual displays, IEICE TRANSACTIONS on Information and Systems 77 (12) (1994) 1321–1329.
[140]
Minsky M., Steps toward artificial intelligence, Proceedings of the IRE 49 (1) (1961) 8–30.
[141]
Mitchell T.M., et al., Machine learning, McGraw-hill New York, 2007.
[142]
Mohamed O.A., Masood S.H., Bhowmik J.L., Optimization of fused deposition modeling process parameters: a review of current research and future prospects, Advances in manufacturing 3 (2015) 42–53.
[143]
Moorhouse N., tom Dieck M.C., Jung T., An experiential view to children learning in museums with augmented reality, Museum Management and Curatorship 34 (4) (2019) 402–418.
[144]
Mouaddib E.M., Pamart A., Pierrot-Deseilligny M., Girardeau-Montaut D., 2D/3D data fusion for the comparative analysis of the vaults of notre-dame de Paris before and after the fire, Journal of Cultural Heritage (2023).
[145]
Moussouri T., Roussos G., Conducting visitor studies using smartphone-based location sensing, Journal on Computing and Cultural Heritage (JOCCH) 8 (3) (2015) 1–16.
[146]
Mucci F., Mucci N., Diolaiuti F., Lockdown and isolation: psychological aspects of COVID-19 pandemic in the general population, Clinical Neuropsychiatry 17 (2) (2020) 63.
[147]
Museum A., Future World: Where Art Meets Science, 2021, https://www.marinabaysands.com/museum/exhibitions/future-world.html.
[148]
Muthanna A., Ateya A.A., Amelyanovich A., Shpakov M., Darya P., Makolkina M., AR enabled system for cultural heritage monitoring and preservation, in: Internet of things, smart spaces, and next generation networks and systems: 18th international conference, NEW2AN 2018, and 11th conference, ruSMART 2018, st. petersburg, Russia, August 27–29, 2018, proceedings 18, Springer, 2018, pp. 560–571.
[149]
Nallapati R., Zhou B., Gulcehre C., Xiang B., et al., Abstractive text summarization using sequence-to-sequence rnns and beyond, 2016, arXiv preprint arXiv:1602.06023.
[150]
Noehrer L., Gilmore A., Jay C., Yehudi Y., The impact of COVID-19 on digital data practices in museums and art galleries in the UK and the US, Humanities and Social Sciences Communications 8 (1) (2021).
[151]
Nofal E., Panagiotidou G., Reffat R.M., Hameeuw H., Boschloos V., Vande Moere A., Situated tangible gamification of heritage for supporting collaborative learning of young museum visitors, Journal on Computing and Cultural Heritage (JOCCH) 13 (1) (2020) 1–24.
[152]
Noh Y.-G., Hong J.-H., Designing reenacted chatbots to enhance museum experience, Applied Sciences 11 (16) (2021) 7420.
[153]
Onishi T., Kadohira T., Watanabe I., Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity, Science and Technology of Advanced Materials 19 (1) (2018) 649–659.
[154]
Onyeaka H., Anumudu C.K., Al-Sharify Z.T., Egele-Godswill E., Mbaegbu P., COVID-19 pandemic: A review of the global lockdown and its far-reaching effects, Science Progress 104 (2) (2021).
[155]
OpenAI (2023). GPT-4: Technical Report,.
[156]
Pallud J., Impact of interactive technologies on stimulating learning experiences in a museum, Information & Management 54 (4) (2017) 465–478.
[157]
Pataranutaporn P., Danry V., Leong J., Punpongsanon P., Novy D., Maes P., et al., AI-generated characters for supporting personalized learning and well-being, Nature Machine Intelligence 3 (12) (2021) 1013–1022.
[158]
Patel D.K., Sakhaei A.H., Layani M., Zhang B., Ge Q., Magdassi S., Highly stretchable and UV curable elastomers for digital light processing based 3D printing, Advanced Materials 29 (15) (2017).
[159]
Petrelli D., Making virtual reconstructions part of the visit: An exploratory study, Digital Applications in Archaeology and Cultural Heritage 15 (2019).
[160]
Piccialli F., Cuomo S., Cola V.S.d., Casolla G., A machine learning approach for IoT cultural data, Journal of Ambient Intelligence and Humanized Computing (2019) 1–12.
[161]
Piscitelli B., Penfold L., Child-centered practice in museums: Experiential learning through creative play at the ipswich art gallery, Curator: The Museum Journal 58 (3) (2015) 263–280.
[162]
Pistofidis P., Arnaoutoglou F., Ioannakis G., Michailidou N., Karta M., Kiourt C., et al., Design and evaluation of smart-exhibit systems that enrich cultural heritage experiences for the visually impaired, Journal of Cultural Heritage 60 (2023) 1–11.
[163]
Pistofidis P., Ioannakis G., Arnaoutoglou F., Michailidou N., Karta M., Kiourt C., et al., Composing smart museum exhibit specifications for the visually impaired, Journal of Cultural Heritage 52 (2021) 1–10.
[164]
Pitts M.J., Burnett G., Skrypchuk L., Wellings T., Attridge A., Williams M.A., Visual–haptic feedback interaction in automotive touchscreens, Displays 33 (1) (2012) 7–16.
[165]
Piumsomboon T., Day A., Ens B., Lee Y., Lee G., Billinghurst M., Exploring enhancements for remote mixed reality collaboration, in: SIGGRAPH Asia 2017 mobile graphics & interactive applications, Association for Computing Machinery, 2017, pp. 1–5.
[166]
Portugal I., Alencar P., Cowan D., The use of machine learning algorithms in recommender systems: A systematic review, Expert Systems with Applications 97 (2018) 205–227.
[167]
Puddu R., Popescu V., Murroni M., Fadda M., Managing accessible tourist services in a social manner, in: 2021 ioT vertical and topical summit for tourism, IEEE, 2021, pp. 1–5.
[168]
Ragusa F., Furnari A., Battiato S., Signorello G., Farinella G.M., EGO-CH: Dataset and fundamental tasks for visitors behavioral understanding using egocentric vision, Pattern Recognition Letters 131 (2020) 150–157.
[169]
Ramm R., Heinze M., Kühmstedt P., Christoph A., Heist S., Notni G., Portable solution for high-resolution 3D and color texture on-site digitization of cultural heritage objects, Journal of Cultural Heritage 53 (2022) 165–175.
[170]
Rener R., The 3D printing of tactile maps for persons with visual impairment, in: Universal access in human–computer interaction. designing novel interactions: 11th international conference, UAHCI 2017, held as part of hCI international 2017, vancouver, BC, Canada, July 9–14, 2017, proceedings, part II 11, Springer, 2017, pp. 335–350.
[171]
Renjith S., Sreekumar A., Jathavedan M., An extensive study on the evolution of context-aware personalized travel recommender systems, Information Processing & Management 57 (1) (2020).
[172]
Roberts J., Banerjee A., Hong A., McGee S., Horn M., Matcuk M., Digital exhibit labels in museums: promoting visitor engagement with cultural artifacts, in: Proceedings of the 2018 CHI conference on human factors in computing systems, 2018, pp. 1–12.
[173]
Rombach R., Blattmann A., Lorenz D., Esser P., Ommer B., High-resolution image synthesis with latent diffusion models, 2021, arXiv:2112.10752.
[174]
Rossetti V., Furfari F., Leporini B., Pelagatti S., Quarta A., Enabling access to cultural heritage for the visually impaired: an interactive 3D model of a cultural site, Procedia computer science 130 (2018) 383–391.
[175]
Russell S.J., Artificial intelligence a modern approach, Pearson Education, Inc., 2010.
[176]
Samaroudi M., Echavarria K.R., Perry L., Heritage in lockdown: digital provision of memory institutions in the UK and US of america during the COVID-19 pandemic, Museum Management and Curatorship 35 (4) (2020) 337–361.
[177]
Sammut C., Webb G.I., Encyclopedia of machine learning, Springer Science & Business Media, 2011.
[178]
Sandron D., Tallon A., Notre Dame Cathedral: Nine centuries of history, Penn State University Press, 2020, URL https://books.google.co.jp/books?id=T_LcDwAAQBAJ.
[179]
Scavarelli A., Arya A., Teather R.J., Virtual reality and augmented reality in social learning spaces: a literature review, Virtual Reality 25 (2021) 257–277.
[180]
Schuettpelz E., Frandsen P.B., Dikow R.B., Brown A., Orli S., Peters M., et al., Applications of deep convolutional neural networks to digitized natural history collections, Biodiversity Data Journal 5 (2017).
[181]
Scianna A., Gaglio G., Grima R., La Guardia M., The virtualization of CH for historical reconstruction: The AR fruition of the fountain of st. George square in valletta (malta), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 44 (2020) 143–149.
[182]
Shahrubudin N., Lee T.C., Ramlan R., An overview on 3D printing technology: Technological, materials, and applications, Procedia Manufacturing 35 (2019) 1286–1296.
[183]
Shani G., Gunawardana A., Evaluating recommendation systems, Recommender Systems Handbook (2011) 257–297.
[184]
Sherman W.R., Craig A.B., Understanding virtual reality: Interface, application, and design, Morgan Kaufmann, 2018.
[185]
Shin J., Cho J., Lee S., Please touch color: Tactile-color texture design for the visually impaired, in: Extended abstracts of the 2020 CHI conference on human factors in computing systems, 2020, pp. 1–7.
[186]
Shiomi, M., Kanda, T., Ishiguro, H., & Hagita, N. (2006). Interactive humanoid robots for a science museum. In Proceedings of the 1st ACM SIGCHI/sIGART conference on human-robot interaction (pp. 305–312).
[187]
Siau K., Wang W., Building trust in artificial intelligence, machine learning, and robotics, Cutter Business Technology Journal 31 (2) (2018) 47–53.
[188]
Singh D.D., Mahender T., Reddy A.R., Powder bed fusion process: A brief review, Materials Today: Proceedings 46 (2021) 350–355.
[189]
Skylar-Scott M.A., Mueller J., Visser C.W., Lewis J.A., Voxelated soft matter via multimaterial multinozzle 3D printing, Nature 575 (7782) (2019) 330–335.
[190]
Smith R., How artificial intelligence could revolutionize archival museum research, 2017.
[191]
Smith R.C., Iversen O.S., Participatory heritage innovation: designing dialogic sites of engagement, Digital Creativity 25 (3) (2014) 255–268.
[192]
Smithsonian Institution R.C., 3D digitization program, 2021, https://3d.si.edu/.
[193]
Smørdal O., Stuedahl D., Sem I., Experimental zones: two cases of exploring frames of participation in a dialogic museum, Digital Creativity 25 (3) (2014) 224–232.
[194]
Sø rensen L.Y., Hansen J.P., A low-cost virtual reality wheelchair simulator, in: Proceedings of the 10th international conference on pErvasive technologies related to assistive environments, 2017, pp. 242–243.
[195]
Sommerauer P., Müller O., Augmented reality in informal learning environments: A field experiment in a mathematics exhibition, Computers & Education 79 (2014) 59–68.
[196]
Srinivasan R., Boast R., Furner J., Becvar K.M., Digital museums and diverse cultural knowledges: Moving past the traditional catalog, The Information Society 25 (4) (2009) 265–278.
[197]
Standard A., ISO/ASTM 52900: 2015 additive manufacturing-general principles-terminology, ASTM F2792-10e1 (2012).
[198]
Suh A., Prophet J., The state of immersive technology research: A literature analysis, Computers in Human Behavior 86 (2018) 77–90.
[199]
Sun S., Brandt M., Easton M., Powder bed fusion processes: An overview, Laser additive manufacturing (2017) 55–77.
[200]
Sutton R.S., Barto A.G., Reinforcement learning: An introduction, MIT Press, 2018.
[201]
Tache A.V., Sandu I.C.A., POPESCU O.-C., PETRIŞOR A.-I., UAV solutions for the protection and management of cultural heritage. case study: Halmyris archaeological site, International Journal of Conservation Science 9 (4) (2018).
[202]
Taigman Y., Polyak A., Wolf L., Unsupervised cross-domain image generation, 2016, arXiv preprint arXiv:1611.02200.
[203]
Tan M.K.B., Tan C.M., Curating wellness during a pandemic in Singapore: COVID-19, museums, and digital imagination, Public Health 192 (2021) 68–71.
[204]
Tang Y.M., Chau K.Y., Kwok A.P.K., Zhu T., Ma X., A systematic review of immersive technology applications for medical practice and education-trends, application areas, recipients, teaching contents, evaluation methods, and performance, Educational Research Review 35 (2022).
[205]
Thorp H.H., ChatGPT is fun, but not an author, Science 379 (6630) (2023) 313.
[206]
Tom Dieck M.C., Jung T.H., tom Dieck D., Enhancing art gallery visitors’ learning experience using wearable augmented reality: generic learning outcomes perspective, Current Issues in Tourism 21 (17) (2018) 2014–2034.
[207]
Torres-Ruiz M., Mata F., Zagal R., Guzmán G., Quintero R., Moreno-Ibarra M., A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques, Virtual Reality 24 (2020) 175–189.
[208]
Touvron H., Lavril T., Izacard G., Martinet X., Lachaux M.-A., Lacroix T., et al., Llama: Open and efficient foundation language models, 2023, arXiv preprint arXiv:2302.13971.
[209]
Trahanias P., Burgard W., Argyros A., Hahnel D., Baltzakis H., Pfaff P., et al., TOURBOT and webfair: Web-operated mobile robots for tele-presence in populated exhibitions, IEEE Robotics & Automation Magazine 12 (2) (2005) 77–89.
[210]
Trichopoulos, G. (2023). Large language models for cultural heritage. In Proceedings of the 2nd international conference of the ACM greek SIGCHI chapter (pp. 1–5).
[211]
Trinitatova D., Tsetserukou D., Touchvr: A wearable haptic interface for VR aimed at delivering multi-modal stimuli at the user’s palm, in: SIGGRAPH Asia 2019 XR, 2019, pp. 42–43.
[212]
Trunfio M., Campana S., Magnelli A., Measuring the impact of functional and experiential mixed reality elements on a museum visit, Current Issues in Tourism 23 (16) (2020) 1990–2008.
[213]
Turner H., Resch G., Southwick D., McEwen R., Dubé A.K., Record I., Using 3D printing to enhance understanding and engagement with young audiences: lessons from workshops in a museum, Curator: The Museum Journal 60 (3) (2017) 311–333.
[214]
Underhill J., In conversation with cyark: digital heritage in the 21st century, International Journal for Digital Art History (3) (2018).
[215]
Usuda-Sato K., Nakayama H., Fujiwara H., Usuda T., Touch the universe: Developing and disseminating tactile telescope models created with a 3D printer, Communicating Astronomy with the Public Journal 26 (4) (2019) 24–30.
[216]
Vahdat-Nejad H., Navabi M.S., Khosravi-Mahmouei H., A context-aware museum-guide system based on cloud computing, International Journal of Cloud Applications and Computing (IJCAC) 8 (4) (2018) 1–19.
[217]
Varitimiadis S., Kotis K., Pittou D., Konstantakis G., Graph-based conversational AI: Towards a distributed and collaborative multi-chatbot approach for museums, Applied Sciences 11 (19) (2021) 9160.
[218]
Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., et al., Attention is all you need, Advances in Neural Information Processing Systems 30 (2017).
[219]
Vaz R., Fernandes P.O., Veiga A.C.R., Designing an interactive exhibitor for assisting blind and visually impaired visitors in tactile exploration of original museum pieces, Procedia computer science 138 (2018) 561–570.
[220]
Vaz R., Fernandes P., Veiga A., Interactive technologies in museums: How digital installations and media are enhancing the visitors’ experience, in: Handbook of research on technological developments for cultural heritage and eTourism applications, IGI Global, 2018, pp. 30–53.
[221]
Verhulst I., Woods A., Whittaker L., Bennett J., Dalton P., Do VR and AR versions of an immersive cultural experience engender different user experiences?, Computers in Human Behavior 125 (2021).
[222]
Vi C.T., Ablart D., Gatti E., Velasco C., Obrist M., Not just seeing, but also feeling art: Mid-air haptic experiences integrated in a multisensory art exhibition, International Journal of Human-Computer Studies 108 (2017) 1–14.
[223]
Voorsluys W., Broberg J., Buyya R., Introduction to cloud computing, Cloud computing: Principles and paradigms (2011) 1–41.
[224]
Voulodimos A., Doulamis N., Doulamis A., Protopapadakis E., et al., Deep learning for computer vision: A brief review, Computational Intelligence and Neuroscience 2018 (2018).
[225]
Weinland D., Ronfard R., Boyer E., A survey of vision-based methods for action representation, segmentation and recognition, Computer Vision and Image Understanding 115 (2) (2011) 224–241.
[226]
Wilson L., Rawlinson A., Frost A., Hepher J., 3D digital documentation for disaster management in historic buildings: Applications following fire damage at the mackintosh building, the glasgow school of art, Journal of Cultural Heritage 31 (2018) 24–32.
[227]
Wilson P.F., Stott J., Warnett J.M., Attridge A., Smith M.P., Williams M.A., Evaluation of touchable 3D-printed replicas in museums, Curator: The Museum Journal 60 (4) (2017) 445–465.
[228]
Wilson P.F., Stott J., Warnett J.M., Attridge A., Smith M.P., Williams M.A., et al., Museum visitor preference for the physical properties of 3D printed replicas, Journal of Cultural Heritage 32 (2018) 176–185.
[229]
Wojciechowski R., Walczak K., White M., Cellary W., Building virtual and augmented reality museum exhibitions, in: Proceedings of the ninth international conference on 3D web technology, 2004, pp. 135–144.
[230]
Wong K.V., Hernandez A., A review of additive manufacturing, International scholarly research notices 2012 (2012).
[231]
Wu T., He S., Liu J., Sun S., Liu K., Han Q.-L., et al., A brief overview of ChatGPT: The history, status quo and potential future development, IEEE/CAA Journal of Automatica Sinica 10 (5) (2023) 1122–1136.
[232]
Xiao W., Mills J., Guidi G., Rodríguez-Gonzálvez P., Barsanti S.G., González-Aguilera D., Geoinformatics for the conservation and promotion of cultural heritage in support of the UN sustainable development goals, ISPRS Journal of Photogrammetry and Remote Sensing 142 (2018) 389–406.
[233]
Xie N., Zhao T., Tian F., Zhang X.H., Sugiyam M., Stroke-based stylization learning and rendering with inverse reinforcement learning, 2015.
[234]
Xu S., Wang J., Shou W., Ngo T., Sadick A.-M., Wang X., Computer vision techniques in construction: a critical review, Archives of Computational Methods in Engineering 28 (2021) 3383–3397.
[235]
Yamazaki A., Yamazaki K., Burdelski M., Kuno Y., Fukushima M., Coordination of verbal and non-verbal actions in human–robot interaction at museums and exhibitions, Journal of Pragmatics 42 (9) (2010) 2398–2414.
[236]
Yang W., Artificial intelligence education for young children: Why, what, and how in curriculum design and implementation, Computers and Education: Artificial Intelligence 3 (2022).
[237]
Yegnanarayana B., Artificial neural networks, PHI Learning Pvt. Ltd., 2009.
[238]
Yoon S.A., Anderson E., Park M., Elinich K., Lin J., How augmented reality, textual, and collaborative scaffolds work synergistically to improve learning in a science museum, Research in Science & Technological Education 36 (3) (2018) 261–281.
[239]
Yoon S.A., Elinich K., Wang J., Steinmeier C., Tucker S., Using augmented reality and knowledge-building scaffolds to improve learning in a science museum, International Journal of Computer-Supported Collaborative Learning 7 (2012) 519–541.
[240]
Young T., Hazarika D., Poria S., Cambria E., Recent trends in deep learning based natural language processing, IEEE Computational Intelligence Magazine 13 (3) (2018) 55–75.
[241]
Zheng X., Chen T.-T., Guo X., Samitsu S., Watanabe I., Controllable inverse design of auxetic metamaterials using deep learning, Materials & Design 211 (2021).
[242]
Zheng X., Chen T.-T., Jiang X., Naito M., Watanabe I., Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized voronoi lattices, Science and Technology of Advanced Materials 24 (1) (2023).
[243]
Zheng X., Fu Z., Du K., Wang C., Yi Y., Minimal surface designs for porous materials: from microstructures to mechanical properties, Journal of materials science 53 (2018) 10194–10208.
[244]
Zheng X., Guo X., Watanabe I., A mathematically defined 3D auxetic metamaterial with tunable mechanical and conduction properties, Materials & Design 198 (2021).
[245]
Zheng X., Guo X., Yang Y., Fu Z., Du K., Wang C., et al., Structure-dependent analysis of nanoporous metals: clues from mechanical, conduction, and flow properties, The Journal of Physical Chemistry C 122 (29) (2018) 16803–16809.
[246]
Zheng X., Uto K., Hu W.-H., Chen T.-T., Naito M., Watanabe I., Reprogrammable flexible mechanical metamaterials, Applied Materials Today 29 (2022).
[247]
Zheng X., Watanabe I., Paik J., Li J., Guo X., Naito M., Text-to-microstructure generation using generative deep learning, Small (2024).
[248]
Zheng X., Watanabe I., Wang S., Chen T.-T., Naito M., Minimal-surface-based multiphase metamaterials with highly variable stiffness, Materials & Design 237 (2024).
[249]
Zheng X., Zhang X., Chen T.-T., Watanabe I., Deep learning in mechanical metamaterials: From prediction and generation to inverse design, Advanced Materials 35 (45) (2023).
[250]
Zhou Y., Chen J., Wang M., A meta-analytic review on incorporating virtual and augmented reality in museum learning, Educational Research Review (2022).
[251]
Zhou L., Zheng X., Du K., Guo X., Yin Q., Lu A., et al., Parametric and experiment studies of 3D auxetic lattices based on hollow shell cuboctahedron, Smart Materials and Structures 30 (2) (2021).
[252]
Ziaee M., Crane N.B., Binder jetting: A review of process, materials, and methods, Additive Manufacturing 28 (2019) 781–801.
[253]
Ziegler M.J., Perez V.J., Pirlo J., Narducci R.E., Moran S.M., Selba M.C., et al., Applications of 3D paleontological data at the florida museum of natural history, Frontiers in Earth Science 8 (2020).

Index Terms

  1. A systematic review of digital transformation technologies in museum exhibition
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image Computers in Human Behavior
    Computers in Human Behavior  Volume 161, Issue C
    Dec 2024
    512 pages

    Publisher

    Elsevier Science Publishers B. V.

    Netherlands

    Publication History

    Published: 18 November 2024

    Author Tags

    1. Digital transformation technologies
    2. Museum exhibitions
    3. Cultural heritage
    4. Visitor experience
    5. Technology and application scenario

    Qualifiers

    • Research-article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 0
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 24 Nov 2024

    Other Metrics

    Citations

    View Options

    View options

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media