{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:08:08Z","timestamp":1772449688975,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Increasing turbulence in contemporary business environments has made the quantitative analysis of unstructured textual data a central methodological challenge for researchers and decision-makers. The increasing availability of large-scale textual data has heightened the need for quantitative frameworks that can transform unstructured language into analyzable numerical representations. Transformer-based language models address this need by encoding text into high-dimensional semantic embeddings. Yet, these representations are commonly treated as black-box inputs for downstream tasks, with limited examination of their intrinsic numerical and geometric properties. The research in this manuscript addresses this gap by proposing a quantitative framework for analyzing transformer-based semantic embeddings as high-dimensional metric spaces prior to task-specific modeling. We employ an innovative methodological approach, considering vector norms regarding examining the dispersion of vector norms to detect concentration of measure, cosine similarity in the context of evaluating the distribution of pairwise cosines between vectors, and principal component analysis. For the purpose of the research, 3034 visitor-generated reviews related to national park experiences were used. Textual inputs are deterministically mapped into a normalized 384-dimensional embedding space using a transformer-based encoder. The analysis examines numerical stability through vector norm dispersion, semantic organization via cosine similarity distributions, variance structure using principal component analysis, and internal organization through unsupervised clustering validity metrics. Clustering is successful when high separation between clusters and high cohesion within clusters are achieved, which is why a single measure combining separation and cohesion metrics was proposed in the research. The results show almost perfect norm stability, backing up the choice of angular similarity as the right semantic metric. Variance decomposition and clustering results share a continuous high-dimensional semantic structure with no dominant latent components or clearly separable clusters. These results suggest that semantic meaning is best thought of as a continuous metric space rather than discrete categories, highlighting the need for representational diagnostics before predictive modeling.<\/jats:p>","DOI":"10.3390\/axioms15030175","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T10:24:34Z","timestamp":1772447074000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Transformer-Based Semantic Encoding Framework for Quantitative Analysis of Large-Scale Textual Reviews"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5308-2503","authenticated-orcid":false,"given":"Darjan","family":"Karaba\u0161evi\u0107","sequence":"first","affiliation":[{"name":"College of Global Business, Korea University, Sejong 30019, Republic of Korea"},{"name":"Faculty of Applied Management, Economics and Finance in Belgrade, University Business Academy in Novi Sad, Jevrejska 24, 11000 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-4228","authenticated-orcid":false,"given":"Aleksandra","family":"Vujko","sequence":"additional","affiliation":[{"name":"Faculty of Tourism and Hospitality Management, Singidunum University, 11000 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6675-5025","authenticated-orcid":false,"given":"Vuk","family":"Mir\u010deti\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Applied Management, Economics and Finance in Belgrade, University Business Academy in Novi Sad, Jevrejska 24, 11000 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2652-4860","authenticated-orcid":false,"given":"Gabrijela","family":"Popovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Applied Management, Economics and Finance in Belgrade, University Business Academy in Novi Sad, Jevrejska 24, 11000 Belgrade, Serbia"},{"name":"Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6846-3074","authenticated-orcid":false,"given":"Dragi\u0161a","family":"Stanujki\u0107","sequence":"additional","affiliation":[{"name":"University College, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea"},{"name":"Technical Faculty in Bor, University of Belgrade Vojske, Jugoslavije 12, 19210 Bor, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.jbusres.2022.02.016","article-title":"Analytics of social media data\u2014State of characteristics and application","volume":"144","author":"Zachlod","year":"2022","journal-title":"J. Bus. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, F., Zhang, Y., Zhu, B., Zhu, H., and Yu, Z. (2022). Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review. Mathematics, 10.","DOI":"10.3390\/math10193554"},{"key":"ref_3","first-page":"137","article-title":"Topic modeling in hospitality and tourism research: Application areas, business insights, and managerial implications","volume":"13","year":"2025","journal-title":"Hotel Tour. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.5937\/jpmnt12-51159","article-title":"Unveiling the characteristics of the EU charismatic leaders using PIPRECIA-S method","volume":"12","year":"2024","journal-title":"J. Process Manag. New Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, J., and Song, C.H. (2025). From Unstructured Feedback to Structured Insight: An LLM-Driven Approach to Value Proposition Modeling. Electronics, 14.","DOI":"10.3390\/electronics14224407"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101014","DOI":"10.1016\/j.ascom.2025.101014","article-title":"Encapsulating textual contents into a MOC data structure for advanced applications","volume":"54","author":"Greco","year":"2026","journal-title":"Astron. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102981","DOI":"10.1016\/j.inffus.2025.102981","article-title":"EHR-based prediction modelling meets multimodal deep learning: A systematic review of structured and textual data fusion methods","volume":"118","author":"Teles","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104808","DOI":"10.1016\/j.cose.2025.104808","article-title":"A Bag of Words Model for Efficient Discovery of Roles in Access Control Systems","volume":"162","author":"Blundo","year":"2025","journal-title":"Comput. Secur."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104666","DOI":"10.1016\/j.jrp.2025.104666","article-title":"A discourse on the use of machine learning (ML) in personality psychology: Can we expect ML to predict questionnaire scores from idiographic text-based data?","volume":"119","author":"Schreiber","year":"2025","journal-title":"J. Res. Personal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e35945","DOI":"10.1016\/j.heliyon.2024.e35945","article-title":"Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data","volume":"10","author":"Zhou","year":"2024","journal-title":"Heliyon"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36120","DOI":"10.1109\/ACCESS.2023.3266377","article-title":"A survey of text representation and embedding techniques in nlp","volume":"11","author":"Patil","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100565","DOI":"10.1016\/j.array.2025.100565","article-title":"A multimodal deep learning framework for integrating visual, textual and categorical features in retail price estimation","volume":"28","author":"Redwan","year":"2025","journal-title":"Array"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128285","DOI":"10.1016\/j.ufug.2024.128285","article-title":"Public emotions and visual perception of the East Coast Park in Singapore: A deep learning method using social media data","volume":"94","author":"Yang","year":"2024","journal-title":"Urban For. Urban Green."},{"key":"ref_14","first-page":"445","article-title":"Smart Tourism as a Strategic Response to Challenges of Tourism in the Post-COVID. Sustainable Business Management and Digital Transformation: Challenges and Opportunities in the Post-COVID Era","volume":"562","year":"2022","journal-title":"Lect. Notes Netw. Syst. Springer"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110767","DOI":"10.1016\/j.apacoust.2025.110767","article-title":"Vibration and noise reduction characteristics of double-layer stiffened plates embedded with acoustic black holes","volume":"237","author":"Yao","year":"2025","journal-title":"Appl. Acoust."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pietrasik, M., and Reformat, M.Z. (2023). Probabilistic Coarsening for Knowledge Graph Embeddings. Axioms, 12.","DOI":"10.3390\/axioms12030275"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114507","DOI":"10.1016\/j.knosys.2025.114507","article-title":"Data-driven interpretation of dimensions in an embedding language model based on a reference knowledge graph","volume":"330","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Du, K.-L., Zhang, R., Jiang, B., Zeng, J., and Lu, J. (2025). Understanding Machine Learning Principles: Learning, Inference, Generalization, and Computational Learning Theory. Mathematics, 13.","DOI":"10.3390\/math13030451"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.knosys.2018.01.027","article-title":"An integrated machine learning framework for hospital readmission prediction","volume":"146","author":"Jiang","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5937\/jpmnt13-58995","article-title":"Machine learning: Techniques, applications, and metrics for enhanced vehicle performance","volume":"13","author":"Ferhath","year":"2025","journal-title":"J. Process Manag. New Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dinh, T., Wong, H., Lisik, D., Koren, M., Tran, D., Yu, P.S., and Torres-Sospedra, J. (Data Sci. Manag., 2025). Data clustering: A fundamental method in data science and management, Data Sci. Manag., in press.","DOI":"10.1016\/j.dsm.2025.08.001"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, A., Chen, H., Zhang, Z., Yang, M., and Chen, Y.-Y. (2025). EmbTCN-Transformer: An Embedding Temporal Convolutional Network\u2013Transformer Model for Multi-Trajectory Prediction. Mathematics, 13.","DOI":"10.3390\/math13203306"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112404","DOI":"10.1016\/j.knosys.2024.112404","article-title":"Enhancing performance of transformer-based models in natural language understanding through word importance embedding","volume":"304","author":"Hong","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114506","DOI":"10.1016\/j.asoc.2025.114506","article-title":"Contrastive learning with transformers for meta-path-free heterogeneous graph embedding","volume":"188","author":"Noori","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104258","DOI":"10.1016\/j.compbiomed.2021.104258","article-title":"FAD-BERT: Improved prediction of FAD binding sites using pre-training of deep bidirectional transformers","volume":"131","author":"Ho","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108987","DOI":"10.1016\/j.asoc.2022.108987","article-title":"Weighting construction by bag-of-words with similarity-learning and supervised training for classification models in court text documents","volume":"124","author":"Castro","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"124123","DOI":"10.1016\/j.eswa.2024.124123","article-title":"A comprehensive analysis of static word embeddings for Turkish","volume":"252","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_28","first-page":"1","article-title":"A transformer-based deep learning framework with semantic encoding and syntax-aware LSTM for fake electronic news detection","volume":"86","author":"Khan","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"127102","DOI":"10.1016\/j.eswa.2025.127102","article-title":"DE-ESD: Dual encoder-based entity synonym discovery using pre-trained contextual embeddings","volume":"276","author":"Huang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100808","DOI":"10.1016\/j.eij.2025.100808","article-title":"Investigating the performance of DistilBERT and LSTM-CNN models with GloVe embeddings for emotion detection from textual data","volume":"32","author":"Zhao","year":"2025","journal-title":"Egypt. Inform. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104415","DOI":"10.1016\/j.ipm.2025.104415","article-title":"Cognition-aligned frequency filtering for sentence embeddings","volume":"63","author":"Mao","year":"2026","journal-title":"Inf. Process. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104705","DOI":"10.1016\/j.jml.2025.104705","article-title":"A predictive coding model for online sentence processing","volume":"146","author":"Ohams","year":"2026","journal-title":"J. Mem. Lang."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102072","DOI":"10.1016\/j.jbtep.2025.102072","article-title":"Semantic similarity among autobiographical memories is associated with rumination","volume":"90","author":"Matsumoto","year":"2026","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100877","DOI":"10.1016\/j.tfp.2025.100877","article-title":"Computational mining of empirical literature on forest recreation: A semantic-driven topic modeling approach based on advanced contextual embeddings","volume":"20","author":"Saoualih","year":"2025","journal-title":"Trees For. People"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104815","DOI":"10.1016\/j.tourman.2023.104815","article-title":"Catching eyes of social media wanderers: How pictorial and textual cues in visitor-generated content shape users\u2019 cognitive-affective psychology","volume":"100","author":"Fu","year":"2024","journal-title":"Tour. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107754","DOI":"10.1016\/j.neunet.2025.107754","article-title":"Shaping pre-trained language models for task-specific embedding generation via consistency calibration","volume":"191","author":"Gao","year":"2025","journal-title":"Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Stanujki\u0107, D., Karaba\u0161evi\u0107, D., Popovi\u0107, G., Zavadskas, E.K., Sara\u010devi\u0107, M., Stanimirovi\u0107, P.S., Uluta\u015f, A., Katsikis, V.N., and Meidute-Kavaliauskiene, I. (2021). Comparative Analysis of the Simple WISP and Some Prominent MCDM Methods: A Python Approach. Axioms, 10.","DOI":"10.3390\/axioms10040347"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1162\/tacl_a_00298","article-title":"What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models","volume":"8","author":"Ettinger","year":"2020","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/3-540-44503-X_27","article-title":"On the surprising behavior of distance metrics in high dimensional space","volume":"Volume 1973","author":"Vianu","year":"2001","journal-title":"Database Theory\u2014ICDT 2001"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104188","DOI":"10.1016\/j.iccn.2025.104188","article-title":"The visitors\u2019 book as a family-centered care tool: A corpus-based, multi-site study on the implementation of a narrative care practice in ICU","volume":"92","author":"Caronia","year":"2026","journal-title":"Intensive Crit. Care Nurs."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111266","DOI":"10.1016\/j.compbiomed.2025.111266","article-title":"ST-NeRP: Spatial\u2013temporal neural representation learning with prior embedding for patient-specific imaging study","volume":"198","author":"Qiu","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100670","DOI":"10.1016\/j.egyai.2025.100670","article-title":"TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems","volume":"23","author":"Nivarthi","year":"2025","journal-title":"Energy AI"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102111","DOI":"10.1016\/j.jics.2025.102111","article-title":"Assessing the influence of extraction techniques on the phytochemical composition of green coffee (Coffea arabica) using principal component analysis (PCA) and hierarchical cluster analysis (HCA)","volume":"102","author":"Pandhi","year":"2025","journal-title":"J. Indian Chem. Soc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.procs.2025.05.047","article-title":"Financial data reduction and information retention strategy based on principal component analysis (PCA) algorithm","volume":"262","author":"Teng","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104586","DOI":"10.1016\/j.tourman.2022.104586","article-title":"Does the implementation of robots in hotels influence the overall TripAdvisor rating? A text mining analysis from the Industry 5.0 approach","volume":"93","year":"2022","journal-title":"Tour. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104530","DOI":"10.1016\/j.jretconser.2025.104530","article-title":"Mapping digital satisfaction dimensions in mobile fashion retail: Service-Dominant Logic in the Turkish market","volume":"88","author":"Tunca","year":"2026","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e3025","DOI":"10.7717\/peerj-cs.3025","article-title":"Comprehensive review of dimensionality reduction algorithms: Challenges, limitations, and innovative solutions","volume":"11","author":"Wani","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.culher.2025.07.019","article-title":"New AI challenges for cultural heritage protection: A general overview","volume":"75","author":"Colace","year":"2025","journal-title":"J. Cult. Herit."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.tourman.2008.05.010","article-title":"Segmentation: A tourism stakeholder view","volume":"30","author":"Tkaczynski","year":"2009","journal-title":"Tour. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.patcog.2012.07.021","article-title":"An extensive comparative study of cluster validity indices","volume":"46","author":"Arbelaitz","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"109632","DOI":"10.1016\/j.fss.2025.109632","article-title":"A correlation-based fuzzy cluster validity index with secondary options detector","volume":"523","author":"Wiroonsri","year":"2026","journal-title":"Fuzzy Sets Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"114926","DOI":"10.1016\/j.knosys.2025.114926","article-title":"Embedding-based decision support framework for large-scale content analysis","volume":"332","author":"Kamat","year":"2026","journal-title":"Knowl. Based Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"130191","DOI":"10.1016\/j.eswa.2025.130191","article-title":"Sensory-CoKGE: A contextualized knowledge graph embedding framework using language models for converting text-based food attributes into numerical representation","volume":"299","author":"Chang","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108086","DOI":"10.1016\/j.future.2025.108086","article-title":"A formal framework for LLM-assisted automated generation of Zeek signatures from binary artifacts","volume":"175","author":"Greco","year":"2026","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"117098","DOI":"10.1016\/j.chaos.2025.117098","article-title":"Self-spectacularization of tourists in visual social media: A computer vision and deep learning approach to socio-cultural body schemas","volume":"200","author":"Wu","year":"2025","journal-title":"Chaos Solitons Fractals"},{"key":"ref_56","first-page":"100082","article-title":"Automated emotion recognition of students in virtual reality classrooms","volume":"5","author":"Shomoye","year":"2024","journal-title":"Comput. Educ. X Real."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111204","DOI":"10.1016\/j.compag.2025.111204","article-title":"Super-resolve satellite imagery to perform on par with UAV-borne hyperspectral imagery in predicting spring wheat physiological parameters using transformer models","volume":"240","author":"Zhao","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_58","unstructured":"Larsson, C. (2018). 5G Networks: Planning, Design, and Optimization, Elsevier."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Orkphol, K., and Yang, W. (2019). Word sense disambiguation using cosine similarity collaborates with Word2vec and WordNet. Future Internet, 11.","DOI":"10.3390\/fi11050114"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.pnmrs.2011.04.003","article-title":"Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics","volume":"59","author":"McKenzie","year":"2011","journal-title":"Prog. Nucl. Magn. Reson. Spectrosc."},{"key":"ref_61","unstructured":"Palacio-Ni\u00f1o, J.O., and Berzal, F. (2019). Evaluation metrics for unsupervised learning algorithms. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.ins.2017.08.065","article-title":"Unsupervised clustering of service performance behaviors","volume":"422","author":"Yahyaoui","year":"2018","journal-title":"Inf. Sci."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/15\/3\/175\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T10:40:56Z","timestamp":1772448056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/15\/3\/175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["axioms15030175"],"URL":"https:\/\/doi.org\/10.3390\/axioms15030175","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]}}}