{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:36:40Z","timestamp":1770273400365,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing\u2019s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%.<\/jats:p>","DOI":"10.3390\/s23135875","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T05:28:02Z","timestamp":1687757282000},"page":"5875","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8288-5230","authenticated-orcid":false,"given":"Bart\u0142omiej","family":"Ambro\u017ckiewicz","sequence":"first","affiliation":[{"name":"Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"},{"name":"Institute of Production Techniques and Systems, Leuphana University of L\u00fcneburg, Universit\u00e4tsallee 1, 21335 L\u00fcneburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3846-835X","authenticated-orcid":false,"given":"Arkadiusz","family":"Syta","sequence":"additional","affiliation":[{"name":"Department of Computerization and Robotization of Production, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4092-8834","authenticated-orcid":false,"given":"Anthimos","family":"Georgiadis","sequence":"additional","affiliation":[{"name":"Institute of Production Techniques and Systems, Leuphana University of L\u00fcneburg, Universit\u00e4tsallee 1, 21335 L\u00fcneburg, Germany"}]},{"given":"Alexander","family":"Gassner","sequence":"additional","affiliation":[{"name":"Institute of Production Techniques and Systems, Leuphana University of L\u00fcneburg, Universit\u00e4tsallee 1, 21335 L\u00fcneburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9647-8345","authenticated-orcid":false,"given":"Grzegorz","family":"Litak","sequence":"additional","affiliation":[{"name":"Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2309-0726","authenticated-orcid":false,"given":"Nicolas","family":"Meier","sequence":"additional","affiliation":[{"name":"Institute of Production Techniques and Systems, Leuphana University of L\u00fcneburg, Universit\u00e4tsallee 1, 21335 L\u00fcneburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1006\/jsvi.1999.3109","article-title":"Effect of radial internal clearance of a ball bearing on the dynamics of a balanced horizontal rotor","volume":"238","author":"Tiwari","year":"2000","journal-title":"J. Sound Vib."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.ijmecsci.2006.03.006","article-title":"Effect of rotational speed fluctuations on the dynamic behaviour of rolling element bearings with radial clearances","volume":"48","author":"Lioulios","year":"2006","journal-title":"Int. J. Mech. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.mechmachtheory.2005.09.003","article-title":"Nonlinear dynamic response of a balanced rotor supported by rolling element bearings due to radial internal clearance effect","volume":"41","author":"Harsha","year":"2006","journal-title":"Mech. Mach. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108954","DOI":"10.1016\/j.ymssp.2022.108954","article-title":"The influence of the radial internal clearance on the dynamic response of self-aligning ball bearings","volume":"171","author":"Syta","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1006\/jsvi.1999.3108","article-title":"Dynamic response of an unbalanced rotor supported on ball bearings","volume":"238","author":"Tiwari","year":"2000","journal-title":"J. Sound Vib."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jsv.2005.10.005","article-title":"Dynamic model of a ball bearings with internal clearance and waviness","volume":"294","author":"Changqing","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_7","first-page":"23","article-title":"Analysis of nonlinear phenomena in high speed ball bearings due to radial clearance and unbalanced rotor effects","volume":"296","author":"Upadhyay","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2298\/TSCI150319083M","article-title":"Analysis of grease contamination influence on the internal radial clearance of ball bearings by thermographic inspection","volume":"20","author":"Miskovic","year":"2016","journal-title":"Therm. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.procir.2022.09.064","article-title":"Effect of thermal expansion on the dynamics of rolling-element bearing","volume":"112","author":"Gassner","year":"2022","journal-title":"Procedia CIRP"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, M., Geng, G., He, Q., Gu, F., and Ball, A. (2020). Vibration characteristics of rolling element bearings with different radial clearances for condition monitoring of wind turbine. Appl. Sci., 10.","DOI":"10.3390\/app10144731"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108280","DOI":"10.1016\/j.ymssp.2021.108280","article-title":"Vibration characteristics and condition monitoring of internal radial clearance within a ball bearing in a gear-shaft-bearing system","volume":"165","author":"Xu","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1007\/s11831-021-09538-1","article-title":"Numerical and experimental studies on performance enhancement of journal bearings using nanoparticles based lubricants","volume":"28","author":"Dang","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1007\/s11831-020-09446-w","article-title":"Review on machine learning algorithm based fault detection in induction motors","volume":"28","author":"Kumar","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103338","DOI":"10.1016\/j.marstruc.2022.103338","article-title":"Underactuated control and analysis of single blade installation using a jackup installation vessel and active tugger line force control","volume":"88","author":"Ren","year":"2023","journal-title":"Mar. Struct."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, Y., Tang, K., and Liu, Y. (2023). A fine-tuning based approach for daily activity recognition between smart homes. Appl. Sci., 13.","DOI":"10.3390\/app13095706"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TCSS.2022.3152091","article-title":"A clinical-oriented non-severe depression diagnosis method based on cognitive behaviour of emotional conflict","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1109\/TR.2022.3180273","article-title":"Intelligent diagnosis using continuous wavelet transform and gauss convolutional belief network","volume":"72","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Reliab."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485128","article-title":"Tackling Climate Change with Machine Learning","volume":"55","author":"Rolnick","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101347","DOI":"10.1016\/j.uclim.2022.101347","article-title":"Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China","volume":"47","author":"Zhang","year":"2023","journal-title":"Urban Clim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s11018-022-02055-y","article-title":"Selection of Reference Circles in the Analysis of Roundness of Rolling Bearings Parts","volume":"65","author":"Zakharov","year":"2022","journal-title":"Meas. Tech."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109074","DOI":"10.1016\/j.ress.2022.109074","article-title":"A variational transformer for predicting turbopump bearing condition under diverse degradation processes","volume":"232","author":"Liu","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1007\/s40544-022-0622-9","article-title":"Dynamic, thermal, and vibrational analysis of ball bearings with over-skidding behaviour","volume":"11","author":"Gao","year":"2023","journal-title":"Friction"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"275","DOI":"10.18178\/ijmerr.11.4.275-280","article-title":"Grease Contamination Detection in the Rolling Element Bearing Using Deep Learning Technique","volume":"11","author":"Sahu","year":"2022","journal-title":"Int. J. Mech. Eng. Robot. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108890","DOI":"10.1016\/j.ress.2022.108890","article-title":"Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions","volume":"230","author":"Ding","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109116","DOI":"10.1016\/j.measurement.2021.109116","article-title":"Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding","volume":"176","author":"Chen","year":"2021","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gr\u0105dzki, R., Bartoszewicz, B., and Martinez, J.E. (2023). Bearing fault diagnostics based on the square of the amplitude gains method. Appl. Sci., 13.","DOI":"10.3390\/app13042160"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TII.2022.3169465","article-title":"Data-Driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network","volume":"19","author":"Ni","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109332","DOI":"10.1016\/j.ress.2023.109332","article-title":"A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks","volume":"237","author":"Zhang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2013.10.002","article-title":"Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell","volume":"18","author":"Safizadeh","year":"2014","journal-title":"Inf. Fusion"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Verstraete, D., Droguett, E., and Modarres, M. (2020). A deep adversarial approach based on multi-sensor fusion for semi-supervised remaining useful life prognostics. Sensors, 20.","DOI":"10.3390\/s20010176"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.matpr.2021.05.447","article-title":"Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis","volume":"51","author":"Nirwan","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s40544-022-0643-4","article-title":"Wear monitoring method of water-lubricated polymer thrust bearing based on ultrasonic reflection coefficient amplitude spectrum","volume":"11","author":"Ning","year":"2023","journal-title":"Friction"},{"key":"ref_34","unstructured":"Nagy, J., and Lakatos, I. (2023). Lecture Notes in Mechanical Engineering, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e202200177","DOI":"10.1002\/elan.202200177","article-title":"Paper-based wearable electrochemical sensors: A new generation of analytical devices","volume":"35","author":"Deroco","year":"2023","journal-title":"Electroanalysis"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.ymssp.2016.11.005","article-title":"A disassembly-free method for evaluation of spiral bevel gear assembly","volume":"88","author":"Jonak","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.engstruct.2007.07.022","article-title":"Gear failure prediction using multiscale local statistics","volume":"30","author":"Loutridis","year":"2008","journal-title":"Eng. Struct."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"12221","DOI":"10.1007\/s13369-021-05930-y","article-title":"Detection of gear wear and faults in spur gear systems using statistical parameters and univariate statistical process control charts","volume":"46","author":"Maras","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.procir.2015.12.110","article-title":"Automatic assembling of bearings including clearance measurement","volume":"41","author":"Meier","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105889","DOI":"10.1016\/j.engappai.2023.105889","article-title":"Improvement of multi-objective evolutionary algorithm and optimization of mechanical bearing","volume":"120","author":"Gao","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104552","DOI":"10.1016\/j.engappai.2021.104552","article-title":"Artificicial intelligence in prognostics and health management of engineering systems","volume":"108","author":"Ochella","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"110187","DOI":"10.1016\/j.ymssp.2023.110187","article-title":"A concise self-adapting deep learning network for machine remaining useful life prediction","volume":"191","author":"Xiang","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108216","DOI":"10.1016\/j.ymssp.2021.108216","article-title":"A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis","volume":"164","author":"Ni","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ymssp.2017.11.029","article-title":"A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery","volume":"108","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Randall, R. (2010). Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications, John Wiley & Sons, Ltd.","DOI":"10.1002\/9780470977668"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"64","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_47","unstructured":"Vinay, V., Kumar, G.V., and Kumar, K.P. (2015, January 12\u201316). Application of chi square feature ranking technique and random forest classifier for fault classification of bearing faults. Proceedings of the 22nd International Congress on Sound and Vibration ICSV 2015, Florence, Italy."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3152","DOI":"10.1177\/10775463211026033","article-title":"Selection of a mother wavelet as identification pattern for the detection of cracks in shafts","volume":"28","author":"Zamorano","year":"2022","journal-title":"J. Vib. Control"},{"key":"ref_49","first-page":"16","article-title":"Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism","volume":"25","author":"Zhang","year":"2023","journal-title":"Eksploat. I Niezawodn.\u2014Maint. Reliab."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. Math. Phys. Eng. Sci."},{"key":"ref_51","unstructured":"Moez, A. (2023, June 17). PyCaret, version 1.0.0; PyCaret: An Open Source, Low-Code Machine Learning Library in Python. Available online: https:\/\/pycaret.org\/."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1205473","DOI":"10.1155\/2021\/1205473","article-title":"Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM","volume":"2021","author":"Xu","year":"2021","journal-title":"Shock. Vib."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"015004","DOI":"10.1088\/1361-6501\/aba93b","article-title":"Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM","volume":"32","author":"Zhang","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Amjad, M., Ahmad, I., Ahmad, M., Wr\u00f3blewski, P., Kami\u0144ski, P., and Amjad, U. (2022). Prediction of pile bearing capacity using XGBoost algorithm: Modelling and performance evaluation. Appl. Sci., 12.","DOI":"10.3390\/app12042126"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Irfan, M., Alwadie, A.S., G\u0142owacz, A., Awais, M., Rahman, S., Khan, M., Jalalah, M., Alshorman, M., and Caesarendra, W. (2021). A novel feature extraction and fault detection technique for the intelligent fault identification of water pump bearings. Sensors, 21.","DOI":"10.3390\/s21124225"},{"key":"ref_56","first-page":"1972","article-title":"Fault detection of anti-friction bearing using ensemble machine learning methods","volume":"31","author":"Patil","year":"2018","journal-title":"Int. J. Eng. Trans. B Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1108\/WJE-12-2017-0403","article-title":"Performance evaluation of bearing degradation based on stationary wavelet decomposition and extra trees regression","volume":"15","author":"Nistane","year":"2018","journal-title":"World J. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"110506","DOI":"10.1016\/j.measurement.2021.110506","article-title":"Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection","volume":"188","author":"Buchaiah","year":"2022","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_59","unstructured":"Gron, A. (2019). Hands-On Machine Learning with Scikit-Learn and Tensor-Flow: Concepts, Tools and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.ymssp.2017.02.013","article-title":"Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump","volume":"93","author":"Zhang","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.sigpro.2016.02.011","article-title":"Variational mode decomposition denoising combined the detrended fluctuation analysis","volume":"125","author":"Liu","year":"2016","journal-title":"Signal Process."},{"key":"ref_62","unstructured":"Margherita, G., Enrico, B., and Giorgio, V. (2020). Metrics for multi-class classification: An overview. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Malhotra, R., and Meena, S. (2021, January 4\u20136). Empirical Validation of cross-version and 10-fold cross-validation for Defect Prediction. Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, Coimbatore, India.","DOI":"10.1109\/ICESC51422.2021.9533030"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Suthar, V., Vakharia, V., Vivek, K.P., and Shah, M. (2022). Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine. Machines, 11.","DOI":"10.3390\/machines11010029"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e454","DOI":"10.1002\/sta4.454","article-title":"K-fold cross-validation for complex sample surveys","volume":"11","author":"Wieczorek","year":"2022","journal-title":"Stat"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5875\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:00:15Z","timestamp":1760126415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,25]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135875"],"URL":"https:\/\/doi.org\/10.3390\/s23135875","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,25]]}}}