{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T05:34:33Z","timestamp":1740548073632,"version":"3.38.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Tensor decomposition algorithms are essential for extracting meaningful latent variables and uncovering hidden structures in real-world data tensors. Unlike conventional deterministic tensor decomposition algorithms, randomized methods offer higher efficiency by reducing memory requirements and computational complexity. This paper proposes an efficient hardware architecture for a randomized tensor decomposition implemented on a field-programmable gate array (FPGA) using high-level synthesis (HLS). The proposed architecture integrates random projection, power iteration, and subspace approximation via QR decomposition to achieve low-rank approximation of multidimensional datasets. The proposed architecture utilizes the capabilities of reconfigurable systems to accelerate tensor computation. It includes three central units: (1) tensor times matrix chain (TTMc), (2) tensor unfolding unit, and (3) QR decomposition unit to implement a three-stage algorithm. Experimental results demonstrate that our FPGA design achieves up to 14.56 times speedup compared to the well-implemented tensor decomposition using software library Tensor Toolbox on an Intel i7-9700 CPU. For a large input tensor of size <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$512 \\times 512 \\times 512$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>512<\/mml:mn>\n                    <mml:mo>\u00d7<\/mml:mo>\n                    <mml:mn>512<\/mml:mn>\n                    <mml:mo>\u00d7<\/mml:mo>\n                    <mml:mn>512<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>, the proposed design achieves a 5.55 times speedup compared to an Nvidia Tesla T4 GPU. Furthermore, we utilize our hardware-based high-order singular value decomposition (HOSVD) accelerator for two real applications: background subtraction of dynamic video datasets and data compression. In both applications, our proposed design shows high efficiency regarding accuracy and computational time.<\/jats:p>","DOI":"10.1007\/s11227-025-07049-5","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T10:42:42Z","timestamp":1740480162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Randomized tensor decomposition using parallel reconfigurable systems"],"prefix":"10.1007","volume":"81","author":[{"given":"Ajita","family":"Misra","sequence":"first","affiliation":[]},{"given":"Muhammad A. A.","family":"Abdelgawad","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Ray C. C.","family":"Cheung","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"issue":"2","key":"7049_CR1","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/TCSVT.2022.3207484","volume":"33","author":"T Wu","year":"2022","unstructured":"Wu T (2022) Online tensor low-rank representation for streaming data clustering. IEEE Trans Circuits Syst Video Technol 33(2):602\u2013617","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7049_CR2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.laa.2004.01.016","volume":"391","author":"L De Lathauwer","year":"2004","unstructured":"De Lathauwer L, Vandewalle J (2004) Dimensionality reduction in higher-order signal processing and rank-(r1, r2, ..., rn) reduction in multilinear algebra. Linear Algebra Appl 391:31\u201355","journal-title":"Linear Algebra Appl"},{"issue":"3","key":"7049_CR3","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455\u2013500","journal-title":"SIAM Rev"},{"key":"7049_CR4","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1146\/annurev-statistics-042720-020816","volume":"8","author":"X Bi","year":"2021","unstructured":"Bi X, Tang X, Yuan Y, Zhang Y, Qu A (2021) Tensors in statistics. Annu Rev Stat Appl 8:345\u2013368","journal-title":"Annu Rev Stat Appl"},{"key":"7049_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2020.101897","volume":"113","author":"R Acosta-Qui\u00f1onez","year":"2021","unstructured":"Acosta-Qui\u00f1onez R, Torres-Roman D, Rodriguez-Avila R (2021) HOSVD prototype based on modular SW libraries running on a high-performance CPU + GPU platform. J Syst Archit 113:101897","journal-title":"J Syst Archit"},{"issue":"3","key":"7049_CR6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02289464","volume":"31","author":"LR Tucker","year":"1966","unstructured":"Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31(3):279\u2013311","journal-title":"Psychometrika"},{"issue":"4","key":"7049_CR7","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253\u20131278","journal-title":"SIAM J Matrix Anal Appl"},{"key":"7049_CR8","doi-asserted-by":"crossref","unstructured":"Sheehan BN, Saad Y (2007) Higher order orthogonal iteration of tensors (HOOI) and its relation to PCA and GLRAM. In: Proceedings of the 2007 SIAM International Conference on Data Mining. SIAM, pp 355\u2013365","DOI":"10.1137\/1.9781611972771.32"},{"issue":"2","key":"7049_CR9","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1137\/110836067","volume":"34","author":"N Vannieuwenhoven","year":"2012","unstructured":"Vannieuwenhoven N, Vandebril R, Meerbergen K (2012) A new truncation strategy for the higher-order singular value decomposition. SIAM J Sci Comput 34(2):1027\u20131052","journal-title":"SIAM J Sci Comput"},{"key":"7049_CR10","doi-asserted-by":"crossref","unstructured":"Kolda TG, Sun J (2008) Scalable tensor decompositions for multi-aspect data mining. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE, pp 363\u2013372","DOI":"10.1109\/ICDM.2008.89"},{"key":"7049_CR11","doi-asserted-by":"publisher","first-page":"13411","DOI":"10.1007\/s11227-024-05945-w","volume":"80","author":"P Diel","year":"2024","unstructured":"Diel P, Mu\u00f1oz-Montoro AJ, Carabias-Orti JJ, Ranilla J (2024) Efficient FPGA implementation for sound source separation using direction-informed multichannel non-negative matrix factorization. J Supercomput 80:13411\u201313433","journal-title":"J Supercomput"},{"issue":"10","key":"7049_CR12","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1109\/TCAD.2015.2513673","volume":"35","author":"R Nane","year":"2015","unstructured":"Nane R, Sima V-M, Pilato C, Choi J, Fort B, Canis A, Chen YT, Hsiao H, Brown S, Ferrandi F et al (2015) A survey and evaluation of FPGA high-level synthesis tools. IEEE Trans Comput Aided Des Integr Circuits Syst 35(10):1591\u20131604","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"issue":"4","key":"7049_CR13","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TCAD.2011.2110592","volume":"30","author":"J Cong","year":"2011","unstructured":"Cong J, Liu B, Neuendorffer S, Noguera J, Vissers K, Zhang Z (2011) High-level synthesis for FPGAs: from prototyping to deployment. IEEE Trans Comput Aided Des Integr Circuits Syst 30(4):473\u2013491","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"key":"7049_CR14","doi-asserted-by":"crossref","unstructured":"Srivastava N, Rong H, Barua P, Feng G, Cao H, Zhang Z, Albonesi D, Sarkar V, Chen W, Petersen P et al (2019) T2s-tensor: productively generating high-performance spatial hardware for dense tensor computations. In: 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, pp 181\u2013189","DOI":"10.1109\/FCCM.2019.00033"},{"key":"7049_CR15","doi-asserted-by":"crossref","unstructured":"Zhang K, Zhang X, Zhang Z (2019) Tucker tensor decomposition on FPGA. In: 2019 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, pp 1\u20138","DOI":"10.1109\/ICCAD45719.2019.8942103"},{"key":"7049_CR16","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.micpro.2018.10.004","volume":"64","author":"W-P Huang","year":"2019","unstructured":"Huang W-P, Kwan BP, Ding W, Min B, Cheung RC, Qi L, Yan H (2019) High performance hardware architecture for singular spectrum analysis of Hankel tensors. Microprocess Microsyst 64:120\u2013127","journal-title":"Microprocess Microsyst"},{"issue":"7","key":"7049_CR17","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1109\/TVLSI.2021.3080318","volume":"29","author":"W-P Huang","year":"2021","unstructured":"Huang W-P, Cheung RC, Yan H (2021) An efficient parallel processor for dense tensor computation. IEEE Trans Very Large Scale Integr (VLSI) Syst 29(7):1335\u20131347","journal-title":"IEEE Trans Very Large Scale Integr (VLSI) Syst"},{"issue":"2","key":"7049_CR18","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TCAD.2021.3058317","volume":"41","author":"Z Qu","year":"2021","unstructured":"Qu Z, Deng L, Wang B, Chen H, Lin J, Liang L, Li G, Zhang Z, Xie Y (2021) Hardware-enabled efficient data processing with tensor-train decomposition. IEEE Trans Comput Aided Des Integr Circuits Syst 41(2):372\u2013385","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"key":"7049_CR19","doi-asserted-by":"crossref","unstructured":"Wijeratne S, Kannan R, Prasanna V (2021) Reconfigurable low-latency memory system for sparse matricized tensor times Khatri\u2013Rao product on FPGA. In: 2021 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, pp 1\u20137","DOI":"10.1109\/HPEC49654.2021.9622851"},{"key":"7049_CR20","doi-asserted-by":"crossref","unstructured":"Srivastava N, Jin H, Smith S, Rong H, Albonesi D, Zhang Z (2020) Tensaurus: a versatile accelerator for mixed sparse-dense tensor computations. In: 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, pp 689\u2013702","DOI":"10.1109\/HPCA47549.2020.00062"},{"key":"7049_CR21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.mejo.2018.11.006","volume":"85","author":"B Min","year":"2019","unstructured":"Min B, Huang W-P, Cheung RC, Yan H (2019) A high performance hardware architecture for non-negative tensor factorization. Microelectron J 85:25\u201333","journal-title":"Microelectron J"},{"key":"7049_CR22","doi-asserted-by":"crossref","unstructured":"Ahmedsaid A, Amira A, Bouridane A (2003) Improved SVD systolic array and implementation on FPGA. In: Proceedings 2003 IEEE International Conference on Field-Programmable Technology (FPT)(IEEE Cat. No. 03EX798). IEEE, pp 35\u201342","DOI":"10.1109\/FPT.2003.1275729"},{"issue":"4","key":"7049_CR23","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1137\/17M1117732","volume":"60","author":"J Dongarra","year":"2018","unstructured":"Dongarra J, Gates M, Haidar A, Kurzak J, Luszczek P, Tomov S, Yamazaki I (2018) The singular value decomposition: anatomy of optimizing an algorithm for extreme scale. SIAM Rev 60(4):808\u2013865","journal-title":"SIAM Rev"},{"key":"7049_CR24","doi-asserted-by":"crossref","unstructured":"Wang Y, Lee J-J, Ding Y, Li P (2020) A scalable FPGA engine for parallel acceleration of singular value decomposition. In: 2020 21st International Symposium on Quality Electronic Design (ISQED). IEEE, pp 370\u2013376","DOI":"10.1109\/ISQED48828.2020.9137055"},{"key":"7049_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2019.100204","volume":"35","author":"B Garcia-Garcia","year":"2020","unstructured":"Garcia-Garcia B, Bouwmans T, Silva AJR (2020) Background subtraction in real applications: challenges, current models and future directions. Comput Sci Rev 35:100204","journal-title":"Comput Sci Rev"},{"key":"7049_CR26","doi-asserted-by":"crossref","unstructured":"Khan S, Xu G, Yan H (2017) Tensor learning using n-mode SVD for dynamic background modelling and subtraction. In: 2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC). IEEE, pp 6\u201310","DOI":"10.1109\/RPC.2017.8168056"},{"issue":"1","key":"7049_CR27","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1089\/brain.2018.0605","volume":"9","author":"F Mokhtari","year":"2019","unstructured":"Mokhtari F, Laurienti PJ, Rejeski WJ, Ballard G (2019) Dynamic functional magnetic resonance imaging connectivity tensor decomposition: a new approach to analyze and interpret dynamic brain connectivity. Brain Connect 9(1):95\u2013112","journal-title":"Brain Connect"},{"issue":"4\u20135","key":"7049_CR28","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1561\/2200000059","volume":"9","author":"A Cichocki","year":"2016","unstructured":"Cichocki A, Lee N, Oseledets I, Phan A-H, Zhao Q, Mandic DP et al (2016) Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions. Found Trends Mach Learn 9(4\u20135):249\u2013429","journal-title":"Found Trends Mach Learn"},{"key":"7049_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2020.113380","volume":"390","author":"M Che","year":"2021","unstructured":"Che M, Wei Y, Yan H (2021) Randomized algorithms for the low multilinear rank approximations of tensors. J Comput Appl Math 390:113380","journal-title":"J Comput Appl Math"},{"issue":"5","key":"7049_CR30","doi-asserted-by":"publisher","first-page":"843","DOI":"10.3390\/electronics9050843","volume":"9","author":"C Souza Junior","year":"2020","unstructured":"Souza Junior C, Bispo J, Cardoso JM, Diniz PC, Marques E (2020) Exploration of FPGA-based hardware designs for QR decomposition for solving stiff ode numerical methods using the harp hybrid architecture. Electronics 9(5):843","journal-title":"Electronics"},{"key":"7049_CR31","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.mejo.2017.10.004","volume":"72","author":"T-T Tseng","year":"2018","unstructured":"Tseng T-T, Shen C-A (2018) Design and implementation of a high-throughput configurable pre-processor for mimo detections. Microelectron J 72:14\u201323","journal-title":"Microelectron J"},{"issue":"3","key":"7049_CR32","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1002\/nla.1839","volume":"20","author":"SJ Leon","year":"2013","unstructured":"Leon SJ, Bj\u00f6rck \u00c5, Gander W (2013) Gram\u2013Schmidt orthogonalization: 100 years and more. Numer Linear Algebra Appl 20(3):492\u2013532","journal-title":"Numer Linear Algebra Appl"},{"key":"7049_CR33","doi-asserted-by":"publisher","DOI":"10.56021\/9781421407944","volume-title":"Matrix Computations","author":"GH Golub","year":"2013","unstructured":"Golub GH, Van Loan CF (2013) Matrix Computations. JHU Press, Baltimore"},{"issue":"9","key":"7049_CR34","doi-asserted-by":"publisher","first-page":"1864","DOI":"10.1109\/TCAD.2020.3032626","volume":"40","author":"W Jiang","year":"2020","unstructured":"Jiang W, Zhang K, Lin CY, Xing F, Zhang Z (2020) Sparse tucker tensor decomposition on a hybrid FPGA\u2013CPU platform. IEEE Trans Comput Aided Des Integr Circuits Syst 40(9):1864\u20131873","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"issue":"11","key":"7049_CR35","doi-asserted-by":"publisher","first-page":"8771","DOI":"10.1007\/s11227-020-03176-3","volume":"76","author":"AE Tom\u00e1s","year":"2020","unstructured":"Tom\u00e1s AE, Quintana-Ort\u00ed ES (2020) Tall-and-skinny QR factorization with approximate householder reflectors on graphics processors. J Supercomput 76(11):8771\u20138786","journal-title":"J Supercomput"},{"key":"7049_CR36","unstructured":"Xilinx, U.: Vivado HLS Optimization Methodology Guide. Apr (2018)"},{"issue":"5","key":"7049_CR37","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1109\/TPDS.2020.3039409","volume":"32","author":"J Fine Licht","year":"2020","unstructured":"Fine Licht J, Besta M, Meierhans S, Hoefler T (2020) Transformations of high-level synthesis codes for high-performance computing. IEEE Trans Parallel Distrib Syst 32(5):1014\u20131029","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"7049_CR38","doi-asserted-by":"crossref","unstructured":"Javed S, Oh SH, Heo J, Jung SK (2014) Robust background subtraction via online robust PCA using image decomposition. In: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems, pp 105\u2013110","DOI":"10.1145\/2663761.2664195"},{"key":"7049_CR39","doi-asserted-by":"crossref","unstructured":"Sobral A, Baker CG, Bouwmans T, Zahzah E-h (2014) Incremental and multi-feature tensor subspace learning applied for background modeling and subtraction. In: Image Analysis and Recognition: 11th International Conference, ICIAR 2014, Vilamoura, Portugal, October 22\u201324, 2014, Proceedings, Part I 11. Springer, pp 94\u2013103","DOI":"10.1007\/978-3-319-11758-4_11"},{"key":"7049_CR40","doi-asserted-by":"crossref","unstructured":"Silva C, Bouwmans T, Fr\u00e9licot C (2015) An extended center-symmetric local binary pattern for background modeling and subtraction in videos. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2015","DOI":"10.5220\/0005266303950402"},{"key":"7049_CR41","doi-asserted-by":"crossref","unstructured":"Giraldo JH, Bouwmans T (2021) GraphBGS: background subtraction via recovery of graph signals. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp 6881\u20136888","DOI":"10.1109\/ICPR48806.2021.9412999"},{"key":"7049_CR42","doi-asserted-by":"crossref","unstructured":"George G, Oommen RM, Shelly S, Philipose SS, Varghese AM (2018) A survey on various median filtering techniques for removal of impulse noise from digital image. In: 2018 Conference on Emerging Devices and Smart Systems (ICEDSS). IEEE, pp 235\u2013238","DOI":"10.1109\/ICEDSS.2018.8544273"},{"key":"7049_CR43","unstructured":"Bader BW, Kolda TG et al (2015) Matlab tensor toolbox version 2.6. http:\/\/www.sandia.gov\/tgkolda\/TensorToolbox"},{"key":"7049_CR44","unstructured":"Kossaifi J, Panagakis Y, Anandkumar A, Pantic M (2016) Tensorly: tensor learning in Python. arXiv preprint arXiv:1610.09555"},{"issue":"7825","key":"7049_CR45","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ et al (2020) Array programming with NumPy. Nature 585(7825):357\u2013362","journal-title":"Nature"},{"key":"7049_CR46","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol 32"},{"key":"7049_CR47","doi-asserted-by":"crossref","unstructured":"Deng C, Yin M, Liu X-Y, Wang X, Yuan B (2019) High-performance hardware architecture for tensor singular value decomposition. In: 2019 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, pp 1\u2013 6","DOI":"10.1109\/ICCAD45719.2019.8942082"},{"issue":"1","key":"7049_CR48","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2009.112","volume":"32","author":"V Mahadevan","year":"2009","unstructured":"Mahadevan V, Vasconcelos N (2009) Spatiotemporal saliency in dynamic scenes. IEEE Trans Pattern Anal Mach Intell 32(1):171\u2013177","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07049-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07049-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07049-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T22:22:19Z","timestamp":1740522139000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07049-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["7049"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07049-5","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"12 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"543"}}