{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:23:26Z","timestamp":1766121806432,"version":"3.48.0"},"reference-count":70,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper introduces a novel quantum-inspired algorithm for numerical multiobjective optimization, uniquely integrating the multilevel structure of qudits with principles of controlled thermonuclear fusion. Moving beyond conventional qubit-based approaches, the algorithm leverages the qudit\u2019s higher-dimensional state space to enhance search capabilities. Fusion-inspired dynamics\u2014modeling particle interaction, energy release, and plasma cooling\u2014provide a powerful metaheuristic framework for navigating complex, high-dimensional Pareto fronts. A hybrid quantum-classical version of the algorithm is presented, designed to exploit the complementary strengths of both computational paradigms for improved efficiency in solving dynamic multiobjective problems. Experimental evaluation on standard dynamic multiobjective benchmarks demonstrates clear performance advantages. Both the quantum-inspired and hybrid variants consistently outperform leading classical algorithms such as NSGA-III, MOEA\/D and GDE3, as well as the quantum-inspired NSGA-III, in key metrics: identifying a greater number of unique non-dominated solutions, ensuring superior uniformity along the Pareto front, maintaining stable convergence across generations, and achieving higher accuracy in approximating the ideal solution.<\/jats:p>","DOI":"10.3390\/a18120793","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T08:46:52Z","timestamp":1765874812000},"page":"793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Thermonuclear Fusion Based Quantum-Inspired Algorithm for Solving Multiobjective Optimization Problems"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4516-3746","authenticated-orcid":false,"given":"Liliya","family":"Demidova","sequence":"first","affiliation":[{"name":"Institute of Information Technologies, Federal State Budget Educational Institution of Higher Education, MIREA\u2014Russian Technological University, 78, Vernadsky Avenue, 119454 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3201-2228","authenticated-orcid":false,"given":"Vladimir","family":"Maslennikov","sequence":"additional","affiliation":[{"name":"Institute of Information Technologies, Federal State Budget Educational Institution of Higher Education, MIREA\u2014Russian Technological University, 78, Vernadsky Avenue, 119454 Moscow, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Grushetskaya, Y., Sips, M., Schachtschneider, R., Saberioon, M., and Mahan, A. (2024, January 9\u201313). HPExplorer: XAI Method to Explore the Relationship Between Hyperparameters and Model Performance. Proceedings of the ECML PKDD 2024, Vilnius, Lithuania.","DOI":"10.1007\/978-3-031-70378-2_20"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"307","DOI":"10.22331\/q-2020-08-13-307","article-title":"Quantum-inspired algorithms in practice","volume":"4","author":"Arrazola","year":"2020","journal-title":"Quantum"},{"key":"ref_3","unstructured":"Abs da Cruz, A.V., Vellasco, M.M.B.R., and Pacheco, M.A.C. (2006, January 16\u201321). Quantum-inspired evolutionary algorithm for numerical optimization. Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hao, T., Huang, X., Jia, C., and Peng, C. (2022). A quantum-inspired tensor network algorithm for constrained combinatorial optimization problems. Front. Phys., 10.","DOI":"10.3389\/fphy.2022.906590"},{"key":"ref_5","first-page":"2019","article-title":"Quantum computing and quantum-inspired techniques for feature subset selection: A review","volume":"67","author":"Chakraborty","year":"2024","journal-title":"Knowl. Inf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s12065-022-00783-2","article-title":"A review of recent advances in quantum-inspired metaheuristics","volume":"17","author":"Hakemi","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10732-010-9136-0","article-title":"Quantum-inspired evolutionary algorithms: A survey and empirical study","volume":"17","author":"Zhang","year":"2011","journal-title":"J. Heuristics"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Iovane, G. (2025). Quantum-Inspired Algorithms and Perspectives for Optimization. Electronics, 14.","DOI":"10.3390\/electronics14142839"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Hu, Z., Sanders, B., and Kais, S. (2020). Qudits and High-Dimensional Quantum Computing. Front. Phys., 8.","DOI":"10.3389\/fphy.2020.589504"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.33693\/2313-223X-2024-11-2-58-85","article-title":"Modification of a Quantum-inspired Genetic Algorithm for Numerical Optimization Using Qudit under Conditions of Simulating Quantum Decoherence","volume":"11","author":"Maslennikov","year":"2024","journal-title":"Comput. Nanotechnol."},{"key":"ref_11","unstructured":"Han, K., and Kim, J. (2000, January 16\u201319). Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary Computation, La Jolla, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1007\/s11128-025-04787-6","article-title":"Quantum-inspired evolutionary algorithms for feature subset selection: A comprehensive survey","volume":"24","author":"Vivek","year":"2025","journal-title":"Quantum Inf. Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3390\/wevj16040221","article-title":"Quantum-Inspired Multi-Objective Optimization Framework for Dynamic Wireless Electric Vehicle Charging in Highway Networks Under Stochastic Traffic and Renewable Energy Variability","volume":"16","author":"Hua","year":"2025","journal-title":"World Electr. Veh. J."},{"key":"ref_14","first-page":"2169","article-title":"A Hybrid Quantum-Classical Generative Adversarial Network for Near-Term Quantum Processors","volume":"12","author":"Nikandish","year":"2024","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105421","DOI":"10.1109\/ACCESS.2022.3210135","article-title":"Decomposition Based Quantum Inspired Salp Swarm Algorithm for Multiobjective Optimization","volume":"10","author":"Pathak","year":"2022","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1016\/j.procs.2023.10.188","article-title":"Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm","volume":"225","author":"Khan","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_17","first-page":"626","article-title":"Two-dimensional delta potential wells and condensed-matter physics","volume":"51","author":"Salazar","year":"2005","journal-title":"Rev. Mex. F\u00edsica"},{"key":"ref_18","first-page":"471","article-title":"A Quantum-Inspired Evolutionary Algorithm Using Gaussian Distribution-Based Quantization","volume":"43","year":"2017","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_19","first-page":"174","article-title":"An Analysis of Hall-of-Fame Strategies in Competitive Coevolutionary Algorithms for Self-Learning in RTS Games","volume":"7997","author":"Nogueira","year":"2013","journal-title":"Learn. Intell. Optim."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, W., Ahmad, I., and Tag-Eldin, E. (2022). Multi-Objective Quantum-Inspired Seagull Optimization Algorithm. Electronics, 11.","DOI":"10.3390\/electronics11121834"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kazimipour, B., Omidvar, M.N., Li, X., and Qin, K. (2014, January 6\u201311). A Novel Hybridization of Opposition-based Learning and Cooperative Co-evolutionary for Large-Scale Optimization. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, Beijing, China.","DOI":"10.1109\/CEC.2014.6900639"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhu, Z., Ning, W., and Fathollahi-Fard, A.M. (2022). An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System. Sustainability, 14.","DOI":"10.3390\/su141710822"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pan, H., and Lu, C. (2024). Solving the Independent Domination Problem by the Quantum Approximate Optimization Algorithm. Entropy, 26.","DOI":"10.3390\/e26121057"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khan, A.A., Hussain, S., and Chandra, R. (2024). A Quantum-Inspired Predator\u2013Prey Algorithm for Real-Parameter Optimization. Algorithms, 17.","DOI":"10.3390\/a17010033"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jansen, D., Heightman, T., Mortimer, L., Perito, I., and Ac\u00edn, A. (2024). Qudit inspired optimization for graph coloring. arXiv.","DOI":"10.1103\/PhysRevApplied.22.064002"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66084","DOI":"10.1109\/ACCESS.2019.2918406","article-title":"Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization","volume":"7","author":"Wei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11227-024-06480-4","article-title":"A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy","volume":"81","author":"Wang","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4570","DOI":"10.1109\/TCYB.2025.3591275","article-title":"Multiobjective Ant Colony Optimization Algorithm Based on Dynamic Constraint Evaluation Strategy for Highly Constrained Optimization","volume":"55","author":"Hou","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112022","DOI":"10.1016\/j.asoc.2024.112022","article-title":"A new prediction-based evolutionary dynamic multiobjective optimization algorithm aided by Pareto optimal solution estimation strategy","volume":"165","author":"Gao","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1111\/j.1741-2005.1955.tb00649.x","article-title":"The Hydrogen Bomb: A Scientist\u2019s Description","volume":"36","author":"Hodgson","year":"1955","journal-title":"Blackfriars"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, X., Qi, X., Deng, L., Chen, A.-X., Wang, H., and Qian, Y. (2025). Uncertainty analysis of the nuclear liquid drop model. Chin. Phys. C.","DOI":"10.1088\/1674-1137\/ae1444"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"082001","DOI":"10.1088\/1361-6587\/ac7b49","article-title":"Thermonuclearizing the plasma focus\u2014Converting plasma focus fusion mechanism from beam-gas target to thermonuclear","volume":"64","author":"Lee","year":"2022","journal-title":"Plasma Phys. Control. Fusion"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"207","DOI":"10.3897\/nucet.9.114267","article-title":"Lawson criterion for different scenarios of using D-3He fuel in fusion reactors","volume":"9","author":"Godes","year":"2023","journal-title":"Nucl. Energy Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"215201","DOI":"10.7498\/aps.68.20190440","article-title":"Numerical simulation of deuterium-tritium fusion reaction rate in laser plasma based on Monte Carlo-discrete ordinate method","volume":"68","author":"Chen","year":"2019","journal-title":"Acta Phys. Sin."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"034003","DOI":"10.1103\/PhysRevC.109.034003","article-title":"Deuterium-tritium fusion \u03b3-ray spectrum at MeV energies with application to reaction-in-flight inertial confinement fusion measurements","volume":"109","author":"Meaney","year":"2024","journal-title":"Phys. Rev. C"},{"key":"ref_36","unstructured":"Fechtal, I. (2024). Simulation of Plasma-Projectile Interaction in the Tokamak Thermonuclear Reactor and Plasma Panels (PDP). [Ph.D. Thesis, Hassan II University of Casablanca]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/978-1-4612-0009-3_15","article-title":"The Density Matrix and Partition Function in Quantum Statistical Mechanics","volume":"27","author":"Williams","year":"2003","journal-title":"Prog. Math. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"052213","DOI":"10.1103\/PhysRevA.111.052213","article-title":"Heisenberg and Heisenberg-Like Representations via Hilbert Space Bundle Geometry in the Non-Hermitian Regime","volume":"111","author":"Ju","year":"2025","journal-title":"Phys. Rev. A"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.pnucene.2012.05.008","article-title":"Fusion energy conversion in magnetically confined plasma reactors","volume":"60","author":"Dobran","year":"2012","journal-title":"Prog. Nucl. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, F. (2000). Introduction to Perturbation Theory in Quantum Mechanics, CRC Press.","DOI":"10.1201\/9781420039641"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"020139","DOI":"10.1103\/PhysRevPhysEducRes.20.020139","article-title":"Challenges in sensemaking and reasoning in the context of degenerate perturbation theory in quantum mechanics","volume":"20","author":"Keebaugh","year":"2024","journal-title":"Phys. Rev. Phys. Educ. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1119\/1.4944701","article-title":"Time-dependent perturbation theory in quantum mechanics and the renormalization group","volume":"84","author":"Bhattacharjee","year":"2016","journal-title":"Am. J. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Raquel, C., and Naval, P. (2005, January 25\u201329). An effective use of crowding distance in multiobjective particle swarm optimization. Proceedings of the GECCO 2005\u2014Genetic and Evolutionary Computation Conference, Washington, DC, USA.","DOI":"10.1145\/1068009.1068047"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1140\/epjqt\/s40507-021-00091-1","article-title":"Commercial applications of quantum computing","volume":"8","author":"Bova","year":"2021","journal-title":"EPJ Quantum Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.21917\/ijme.2025.0333","article-title":"Integration of quantum-inspired algorithms in circuit technologies for enhanced computational efficiency","volume":"10","author":"Joe","year":"2025","journal-title":"ICTACT J. Microelectron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"032209","DOI":"10.1103\/PhysRevA.109.032209","article-title":"Hybrid classical-quantum systems in terms of moments","volume":"109","author":"Brizuela","year":"2024","journal-title":"Phys. Rev. A"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rallis, K., Liliopoulos, I., Tsipas, E., Varsamis, G., Melissourgos, N., Karafyllidis, I., Sirakoulis, G., and Dimitrakis, P. (2025). Hardware-level Interfaces for Hybrid Quantum-Classical Computing Systems. arXiv.","DOI":"10.1109\/EEITE65381.2025.11166221"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/LSP.2022.3164852","article-title":"Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization","volume":"29","author":"Nikoloska","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_49","unstructured":"Pagni, V., Huber, S., Epping, M., and Felderer, M. (2025). Fast Quantum Amplitude Encoding of Typical Classical Data. arXiv."},{"key":"ref_50","unstructured":"Goscinski, A. (2018). The parallel Grover as dynamic system. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"23570","DOI":"10.1038\/s41598-024-74587-y","article-title":"A concept of controlling Grover diffusion operator: A new approach to solve arbitrary Boolean-based problems","volume":"14","author":"Perkowski","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"125305","DOI":"10.1088\/1751-8121\/adbfe5","article-title":"Robustness of quantum symmetries against perturbations","volume":"58","author":"Facchi","year":"2025","journal-title":"J. Phys. A Math. Theor."},{"key":"ref_53","unstructured":"Melo, A., Beugnot, G., and Minganti, F. (2025). Variational Perturbation Theory in Open Quantum Systems for Efficient Steady State Computation. arXiv."},{"key":"ref_54","unstructured":"(2014). IEEE CEC 2025 IEEE Congress on Evolutionary Computation. IEEE Computational Intelligence Magazine, IEEE. Available online: https:\/\/ieeexplore.ieee.org\/document\/10709777."},{"key":"ref_55","unstructured":"Zou, J., Jiang, S., Hou, Z., Yu, X., Hu, Y., and Yang, S. (2025, January 8\u201312). Dynamic Multiobjective Optimisation. Proceedings of the 2025 IEEE Congress on Evolutionary Computation (CEC 2025), Hangzhou, China. Available online: https:\/\/yxz996.github.io."},{"key":"ref_56","first-page":"1","article-title":"Evolutionary Dynamic Multi-Objective Optimization: A Survey","volume":"55","author":"Jiang","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5529","DOI":"10.1109\/TCYB.2024.3364375","article-title":"Learning to Guide Particle Search for Dynamic Multiobjective Optimization","volume":"54","author":"Song","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TETCI.2025.3529882","article-title":"Dual-Population Evolution Based Dynamic Constrained Multiobjective Optimization with Discontinuous and Irregular Feasible Regions","volume":"9","author":"Jiang","year":"2025","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1109\/TEVC.2022.3193287","article-title":"A Correlation-Guided Layered Prediction Approach for Evolutionary Dynamic Multiobjective Optimization","volume":"27","author":"Yu","year":"2022","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1109\/TCYB.2021.3128584","article-title":"Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance","volume":"53","author":"Hu","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_61","first-page":"1","article-title":"Dynamic Multi-Objective Optimization Algorithm Guided by Recurrent Neural Network","volume":"1","author":"Hu","year":"2024","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Doerr, B., and Wietheger, S. (2024, January 14\u201318). A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III). Proceedings of the Genetic and Evolutionary Computation Conference Companion, Melbourne, Australia.","DOI":"10.1145\/3638530.3664062"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1162\/EVCO_a_00109","article-title":"MOEA\/D with adaptive weight adjustment","volume":"22","author":"Qi","year":"2014","journal-title":"Evol. Comput."},{"key":"ref_64","unstructured":"Kukkonen, S., and Lampinen, J. (2005, January 2\u20135). GDE3: The third evolution step of generalized differential evolution. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Edinburgh, UK."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"57","DOI":"10.21667\/1995-4565-2025-92-57-76","article-title":"Application of multilevel quantum systems for parallel evaluation of solutions in multiobjective optimization problems","volume":"92","author":"Demidova","year":"2025","journal-title":"Vestn. Ryazan State Radio Eng. Univ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1007\/s10462-020-09906-6","article-title":"Performance assessment of the metaheuristic optimization algorithms: An exhaustive review","volume":"54","author":"Halim","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_67","unstructured":"(2025, July 26). Quantum Computer Company & Practical Computing Solutions. SpinQ. Available online: https:\/\/www.spinquanta.com\/products\/quantum-computing-software."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1038\/s42005-025-02126-w","article-title":"Quantum-classical hybrid algorithm for solving the learning-with-errors problem on NISQ devices","volume":"8","author":"Zheng","year":"2025","journal-title":"Commun. Phys."},{"key":"ref_69","first-page":"95","article-title":"Quantum Code and Connectivity: Next-Generation Algorithms, Cryptography, and Secure Quantum Networks","volume":"5","author":"Pasupuleti","year":"2025","journal-title":"Int. J. Acad. Ind. Res. Innov. (IJAIRI)"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"76","DOI":"10.32362\/2500-316X-2025-13-1-76-88","article-title":"Optimization of signal constellations with amplitude-phase shift keying in communication channels with non-fluctuating interference","volume":"13","author":"Kulikov","year":"2025","journal-title":"Russ. Technol. J."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:12:06Z","timestamp":1766121126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":70,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["a18120793"],"URL":"https:\/\/doi.org\/10.3390\/a18120793","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,12,15]]}}}