{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T13:34:10Z","timestamp":1780580050744,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To address the limitations of the original Sand Cat Swarm Optimization (SCSO) algorithm\u2014such as static strategy selection, insufficient population diversity, and coarse boundary handling\u2014this paper proposes a multi-strategy enhanced version, namely the Modified Sand Cat Swarm Optimization (MSCSO). The algorithm improves performance through three core strategies: (1) an adaptive strategy selection mechanism that dynamically adapts to different optimization phases; (2) an adaptive crossover\u2013mutation strategy inspired by differential evolution, in which mutation vectors are generated with the guidance of the global best solution and updated via binomial crossover, thereby enhancing both population diversity and local search capability; and (3) a boundary control mechanism guided by the global best solution, which repairs out-of-bound solutions by relocating them between the global best and the boundary, thus preserving useful search information and avoiding oscillation near the limits. To validate the performance of MSCSO, extensive experiments were conducted on the CEC2020 and CEC2022 benchmark suites under 10- and 20-dimensional scenarios, where MSCSO was compared with seven algorithms, including Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The results demonstrate that MSCSO consistently outperforms its competitors on unimodal, multimodal, and hybrid functions. Notably, MSCSO achieved the best Friedman ranking across all dimensions. Ablation studies further confirm that the three proposed strategies exhibit strong synergy, collectively accelerating convergence and enhancing stability. In addition, MSCSO was applied to multilevel threshold image segmentation, where Otsu\u2019s criterion was adopted as the objective function and experiments were conducted on five benchmark images with 4\u201310 thresholds. The results show that MSCSO achieves superior segmentation quality, significantly outperforming the comparison algorithms. Overall, this study demonstrates that MSCSO effectively balances exploration and exploitation without increasing computational complexity, providing not only a powerful tool for global optimization but also a reliable technique for engineering tasks such as multilevel threshold image segmentation. These findings highlight its strong theoretical significance and promising application potential.<\/jats:p>","DOI":"10.3390\/sym17112012","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T14:21:54Z","timestamp":1763648514000},"page":"2012","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MSCSO: A Modified Sand Cat Swarm Optimization for Global Optimization and Multilevel Thresholding Image Segmentation"],"prefix":"10.3390","volume":"17","author":[{"given":"Xuanqi","family":"Yuan","sequence":"first","affiliation":[{"name":"College of Packaging Design Arts, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Graduate School of Government & Business, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Design, Hanyang University, Ansan 15588, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongnian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Packaging Design Arts, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1109\/TMI.2018.2835303","article-title":"DRINet for Medical Image Segmentation","volume":"37","author":"Chen","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101889","DOI":"10.1016\/j.media.2020.101889","article-title":"Capsules for biomedical image segmentation","volume":"68","author":"LaLonde","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6797","DOI":"10.1109\/TCSVT.2023.3295062","article-title":"Self-Supervised Interactive Image Segmentation","volume":"34","author":"Shi","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"116511","DOI":"10.1016\/j.eswa.2022.116511","article-title":"Multi-threshold Image Segmentation using a Multi-strategy Shuffled Frog Leaping Algorithm","volume":"194","author":"Chen","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110130","DOI":"10.1016\/j.asoc.2023.110130","article-title":"A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation","volume":"137","author":"Wang","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102799","DOI":"10.1016\/j.displa.2024.102799","article-title":"Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images","volume":"84","author":"Shi","year":"2024","journal-title":"Displays"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fan, Q., Ma, Y., Wang, P., and Bai, F. (2024). Otsu Image Segmentation Based on a Fractional Order Moth\u2013Flame Optimization Algorithm. Fractal Fract., 8.","DOI":"10.3390\/fractalfract8020087"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1007\/s00371-023-02993-w","article-title":"Multi-threshold image segmentation algorithm based on Aquila optimization","volume":"40","author":"Guo","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zheng, J., Gao, Y., Zhang, H., Lei, Y., and Zhang, J. (2022). OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm. Appl. Sci., 12.","DOI":"10.3390\/app122211514"},{"key":"ref_10","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7711","DOI":"10.1007\/s00521-021-06885-9","article-title":"Adaptive grey wolf optimizer","volume":"34","author":"Meidani","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_13","first-page":"226","article-title":"Group-based synchronous-asynchronous Grey Wolf Optimizer","volume":"93","author":"Camarena","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki, M.H., Zamani, H., and Mirjalili, S. (2022). Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Comput. Biol. Med., 148.","DOI":"10.1016\/j.compbiomed.2022.105858"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107854","DOI":"10.1016\/j.asoc.2021.107854","article-title":"Multi-population improved whale optimization algorithm for high dimensional optimization","volume":"112","author":"Sun","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.renene.2022.02.108","article-title":"A short-term wind power prediction model based on CEEMD and WOA-KELM","volume":"189","author":"Ding","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/s10462-024-10716-3","article-title":"Red-billed blue magpie optimizer: A novel metaheuristic algorithm for 2D\/3D UAV path planning and engineering design problems","volume":"57","author":"Fu","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"025301","DOI":"10.1063\/5.0255039","article-title":"Multi-objective optimal scheduling of islanded microgrid based on ISSA","volume":"17","author":"Lu","year":"2025","journal-title":"J. Renew. Sustain. Energy"},{"key":"ref_20","first-page":"105295","article-title":"Multi-objective feature selection algorithm using Beluga Whale Optimization","volume":"257","author":"Mansouri","year":"2024","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111257","DOI":"10.1016\/j.knosys.2023.111257","article-title":"Crested Porcupine Optimizer: A new nature-inspired metaheuristic","volume":"284","author":"Mohamed","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","article-title":"Ant Colony Optimization","volume":"1","author":"Dorigo","year":"2006","journal-title":"Comput. Intell. Mag. IEEE"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1504\/IJBIC.2013.055093","article-title":"Bat algorithm: Literature review and applications","volume":"5","author":"Yang","year":"2013","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110248","DOI":"10.1016\/j.knosys.2022.110248","article-title":"Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems","volume":"262","author":"Mohamed","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113589","DOI":"10.1016\/j.knosys.2025.113589","article-title":"The Animated Oat Optimization Algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks","volume":"318","author":"Wang","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108320","DOI":"10.1016\/j.knosys.2022.108320","article-title":"Snake Optimizer: A novel meta-heuristic optimization algorithm","volume":"242","author":"Hashim","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113978","DOI":"10.1016\/j.knosys.2025.113978","article-title":"Projection-Iterative-Methods-based Optimizer: A novel metaheuristic algorithm for continuous optimization problems and feature selection","volume":"326","author":"Yu","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","article-title":"Dung beetle optimizer: A new meta-heuristic algorithm for global optimization","volume":"79","author":"Xue","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113377","DOI":"10.1016\/j.eswa.2020.113377","article-title":"Marine Predators Algorithm: A nature-inspired metaheuristic","volume":"152","author":"Faramarzi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.asoc.2019.03.012","article-title":"A new meta-heuristic optimizer: Pathfinder algorithm","volume":"78","author":"Yapici","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","article-title":"Grasshopper Optimisation Algorithm: Theory and application","volume":"105","author":"Saremi","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115665","DOI":"10.1016\/j.eswa.2021.115665","article-title":"Remora optimization algorithm","volume":"185","author":"Jia","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103249","DOI":"10.1016\/j.engappai.2019.103249","article-title":"Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems","volume":"87","author":"Hayyolalam","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107050","DOI":"10.1016\/j.cie.2020.107050","article-title":"Golden eagle optimizer: A nature-inspired metaheuristic algorithm","volume":"152","author":"Nayeri","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"114616","DOI":"10.1016\/j.cma.2022.114616","article-title":"Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization","volume":"392","author":"Zamani","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1007\/s10462-023-10567-4","article-title":"Crayfish optimization algorithm","volume":"56","author":"Jia","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pierezan, J., and Coelho, L.D.S. (2018, January 8\u201313). Coyote Optimization Algorithm: A new metaheuristic for global optimization problems. Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477769"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","article-title":"Chimp optimization algorithm","volume":"149","author":"Khishe","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105082","DOI":"10.1016\/j.engappai.2022.105082","article-title":"Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems","volume":"114","author":"Wang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"120905","DOI":"10.1016\/j.eswa.2023.120905","article-title":"Great Wall Construction Algorithm: A novel meta-heuristic algorithm for engineer problems","volume":"233","author":"Guan","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s10462-025-11291-x","article-title":"Cuckoo catfish optimizer: A new meta-heuristic optimization algorithm","volume":"58","author":"Wang","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s10462-025-11289-5","article-title":"Kirchhoff\u2019s law algorithm (KLA): A novel physics-inspired non-parametric metaheuristic algorithm for optimization problems","volume":"58","author":"Ghasemi","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"130603","DOI":"10.1016\/j.neucom.2025.130603","article-title":"The status-based optimization: Algorithm and comprehensive performance analysis","volume":"647","author":"Wang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Manisha, N., and Kumar, P. (2025). An improved osprey optimization algorithm to analyse the steady state performance of stainless steel utensil manufacturing unit. Life Cycle Reliab. Saf. Eng., 1\u201318.","DOI":"10.1007\/s41872-025-00319-4"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s10462-024-11053-1","article-title":"Modified LSHADE-SPACMA with new mutation strategy and external archive mechanism for numerical optimization and point cloud registration","volume":"58","author":"Fu","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10586-024-04931-4","article-title":"Enhanced zebra optimization algorithm for reliability redundancy allocation and engineering optimization problems","volume":"28","author":"Punia","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10462-024-10821-3","article-title":"Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving","volume":"57","author":"Yu","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1007\/s00366-022-01604-x","article-title":"Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems","volume":"39","author":"Seyyedabbasi","year":"2022","journal-title":"Eng. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1007\/s11831-024-10217-0","article-title":"Advances in Sand Cat Swarm Optimization: A Comprehensive Study","volume":"32","author":"Anka","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10462-024-10986-x","article-title":"Improved sandcat swarm optimization algorithm for solving global optimum problems","volume":"58","author":"Jia","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1007\/s10586-024-05005-1","article-title":"Dhole optimization algorithm: A new metaheuristic algorithm for solving optimization problems","volume":"28","author":"Mohammed","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6855","DOI":"10.1007\/s00521-022-08078-4","article-title":"Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation","volume":"35","author":"Ryalat","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mohamed, A.W., Hadi, A.A., Mohamed, A.K., and Awad, N.H. (2020, January 19\u201324). Evaluating the performance of adaptive gainingsharing knowledge based algorithm on CEC 2020 benchmark problems. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185901"},{"key":"ref_56","unstructured":"Luo, W., Lin, X., Li, C., Yang, S., and Shi, Y. (2022). Benchmark functions for CEC 2022 competition on seeking multiple optima in dynamic environments. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"118208","DOI":"10.1016\/j.cma.2025.118208","article-title":"Holistic swarm optimization: A novel metaphor-less algorithm guided by whole population information for addressing exploration-exploitation dilemma","volume":"445","author":"Akbari","year":"2025","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-024-10729-y","article-title":"Secretary bird optimization algorithm: A new metaheuristic for solving global optimization problems","volume":"57","author":"Fu","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ou, Y., Qin, F., Zhou, K.-Q., Yin, P.-F., Mo, L.-P., and Mohd Zain, A.J.S. (2024). An improved grey wolf optimizer with multi-strategies coverage in wireless sensor networks. Symmetry, 16.","DOI":"10.3390\/sym16030286"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"103694","DOI":"10.1016\/j.advengsoft.2024.103694","article-title":"Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization","volume":"195","author":"Wang","year":"2024","journal-title":"Adv. Eng. Softw."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Cao, L., and Wei, Q.J.B. (2025). SZOA: An Improved Synergistic Zebra Optimization Algorithm for Microgrid Scheduling and Management. Biomimetics, 10.","DOI":"10.3390\/biomimetics10100664"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.asoc.2017.02.005","article-title":"Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images","volume":"55","author":"Suresh","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, J., Zhang, X., and Wang, B. (2025). ACPOA: An Adaptive Cooperative Pelican Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation. Biomimetics, 10.","DOI":"10.3390\/biomimetics10090596"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ins.2016.06.020","article-title":"A cooperative honey bee mating algorithm and its application in multi-threshold image segmentation","volume":"369","author":"Jiang","year":"2016","journal-title":"Inf. Sci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/11\/2012\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T09:13:08Z","timestamp":1764061988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/11\/2012"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":64,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["sym17112012"],"URL":"https:\/\/doi.org\/10.3390\/sym17112012","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]}}}