{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:07:09Z","timestamp":1772644029510,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00371-025-04233-9","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T16:25:34Z","timestamp":1764865534000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced PPE detection in low-light tunnel environments: a YOLOv5-based approach"],"prefix":"10.1007","volume":"42","author":[{"given":"Menglan","family":"Wu","sequence":"first","affiliation":[]},{"given":"Weiwei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Guanxian","family":"Song","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Chuanyi","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yuge","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Nenghao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Menglong","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"4233_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2021.103299","volume":"44","author":"SO Abioye","year":"2021","unstructured":"Abioye, S.O., et al.: Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges. J. Build. Eng. 44, 103299 (2021). https:\/\/doi.org\/10.1016\/j.jobe.2021.103299","journal-title":"J. Build. Eng."},{"key":"4233_CR2","doi-asserted-by":"publisher","first-page":"113580","DOI":"10.1109\/ACCESS.2023.3323588","volume":"11","author":"J Chen","year":"2023","unstructured":"Chen, J., Zhu, J., Li, Z., Yang, X.: YOLOv7-WFD: a novel convolutional neural network model for helmet detection in high-risk workplaces. IEEE Access 11, 113580\u2013113592 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3323588","journal-title":"IEEE Access"},{"key":"4233_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-024-02701-4","author":"Q Liu","year":"2025","unstructured":"Liu, Q.: Application of BP neural network model algorithm in safety risk identification of tunnel construction. Int. J. Syst. Assur. Eng. Manag. (2025). https:\/\/doi.org\/10.1007\/s13198-024-02701-4","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"issue":"3","key":"4233_CR4","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1108\/SASBE-10-2022-0224","volume":"14","author":"A Rashidi","year":"2025","unstructured":"Rashidi, A., Woon, G.L., Dasandara, M., Bazghaleh, M., Pasbakhsh, P.: Smart personal protective equipment for intelligent construction safety monitoring. SASBE 14(3), 835\u2013858 (2025). https:\/\/doi.org\/10.1108\/SASBE-10-2022-0224","journal-title":"SASBE"},{"key":"4233_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2023.106368","volume":"170","author":"S Rasouli","year":"2024","unstructured":"Rasouli, S., Alipouri, Y., Chamanzad, S.: Smart personal protective equipment (PPE) for construction safety: a literature review. Saf. Sci. 170, 106368 (2024). https:\/\/doi.org\/10.1016\/j.ssci.2023.106368","journal-title":"Saf. Sci."},{"issue":"1","key":"4233_CR6","doi-asserted-by":"publisher","first-page":"50","DOI":"10.47412\/LXQC9076","volume":"45","author":"J Hayles","year":"2022","unstructured":"Hayles, J., Alli, K.S., Haninph, L.A.: A convolutional neural network based robust automated real-time image detection system for personal protective equipment. WIJE 45(1), 50\u201361 (2022). https:\/\/doi.org\/10.47412\/LXQC9076","journal-title":"WIJE"},{"issue":"6","key":"4233_CR7","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4233_CR8","doi-asserted-by":"publisher","first-page":"e999","DOI":"10.7717\/peerj-cs.999","volume":"8","author":"M Ferdous","year":"2022","unstructured":"Ferdous, M., Ahsan, Sk.: PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites. PeerJ Comput. Sci. 8, e999 (2022). https:\/\/doi.org\/10.7717\/peerj-cs.999","journal-title":"PeerJ Comput. Sci."},{"issue":"10","key":"4233_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/s21103478","volume":"21","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Wu, Y., Yang, L., Thirunavukarasu, A., Evison, C., Zhao, Y.: Fast personal protective equipment detection for real construction sites using deep learning approaches. Sensors 21(10), 3478 (2021). https:\/\/doi.org\/10.3390\/s21103478","journal-title":"Sensors"},{"key":"4233_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103828","volume":"130","author":"R Xiong","year":"2021","unstructured":"Xiong, R., Tang, P.: Pose guided anchoring for detecting proper use of personal protective equipment. Autom. Constr. 130, 103828 (2021). https:\/\/doi.org\/10.1016\/j.autcon.2021.103828","journal-title":"Autom. Constr."},{"key":"4233_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103356","volume":"120","author":"S Tang","year":"2020","unstructured":"Tang, S., Roberts, D., Golparvar-Fard, M.: Human-object interaction recognition for automatic construction site safety inspection. Autom. Constr. 120, 103356 (2020). https:\/\/doi.org\/10.1016\/j.autcon.2020.103356","journal-title":"Autom. Constr."},{"issue":"35","key":"4233_CR12","doi-asserted-by":"publisher","first-page":"82621","DOI":"10.1007\/s11042-024-18772-1","volume":"83","author":"R Qiao","year":"2024","unstructured":"Qiao, R., Cai, C., Meng, H., Wu, K., Wang, F., Zhao, J.: An improved personal protective equipment detection method based on YOLOv4. Multimed. Tools Appl. 83(35), 82621\u201382639 (2024). https:\/\/doi.org\/10.1007\/s11042-024-18772-1","journal-title":"Multimed. Tools Appl."},{"key":"4233_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2025.105355","volume":"166","author":"Y Li","year":"2025","unstructured":"Li, Y., Li, X., Lin, M.: Fe-yolo: fourier enhancement YOLO for end-to-end object detection in low-light conditions. Digit. Signal Process. 166, 105355 (2025). https:\/\/doi.org\/10.1016\/j.dsp.2025.105355","journal-title":"Digit. Signal Process."},{"issue":"10","key":"4233_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/buildings15101609","volume":"15","author":"S Malaikrisanachalee","year":"2025","unstructured":"Malaikrisanachalee, S., Wongwai, N., Kowcharoen, E.: Espcn-yolo: a high-accuracy framework for personal protective equipment detection under low-light and small object conditions. Buildings 15(10), 1609 (2025). https:\/\/doi.org\/10.3390\/buildings15101609","journal-title":"Buildings"},{"key":"4233_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.102510","volume":"23","author":"K Vinoth","year":"2024","unstructured":"Vinoth, K., P, S.: Lightweight object detection in low light: pixel-wise depth refinement and TensorRT optimization. Results Eng. 23, 102510 (2024). https:\/\/doi.org\/10.1016\/j.rineng.2024.102510","journal-title":"Results Eng."},{"issue":"16","key":"4233_CR16","doi-asserted-by":"publisher","first-page":"3059","DOI":"10.3390\/rs13163059","volume":"13","author":"J Hu","year":"2021","unstructured":"Hu, J., Zhi, X., Shi, T., Zhang, W., Cui, Y., Zhao, S.: PAG-YOLO: a portable attention-guided YOLO network for small ship detection. Remote Sens. 13(16), 3059 (2021). https:\/\/doi.org\/10.3390\/rs13163059","journal-title":"Remote Sens."},{"issue":"23","key":"4233_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/rs13234851","volume":"13","author":"M Kim","year":"2021","unstructured":"Kim, M., Jeong, J., Kim, S.: Ecap-yolo: efficient channel attention pyramid YOLO for small object detection in aerial image. Remote Sens. 13(23), 4851 (2021). https:\/\/doi.org\/10.3390\/rs13234851","journal-title":"Remote Sens."},{"issue":"11","key":"4233_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11111673","volume":"11","author":"R Li","year":"2022","unstructured":"Li, R., Wu, Y.: Improved YOLO v5 wheat ear detection algorithm based on attention mechanism. Electronics 11(11), 1673 (2022). https:\/\/doi.org\/10.3390\/electronics11111673","journal-title":"Electronics"},{"key":"4233_CR19","doi-asserted-by":"publisher","DOI":"10.20944\/preprints202405.0758.v1","author":"H Liu","year":"2024","unstructured":"Liu, H., Qin, X.: Target detection of safety protective gear using the improved YOLOv5. Comput. Sci. Math. (2024). https:\/\/doi.org\/10.20944\/preprints202405.0758.v1","journal-title":"Comput. Sci. Math."},{"issue":"12","key":"4233_CR20","doi-asserted-by":"publisher","first-page":"8927","DOI":"10.1007\/s00371-024-03284-8","volume":"40","author":"K Tong","year":"2024","unstructured":"Tong, K., Wu, Y.: I-YOLO: a novel single-stage framework for small object detection. Vis. Comput. 40(12), 8927\u20138944 (2024). https:\/\/doi.org\/10.1007\/s00371-024-03284-8","journal-title":"Vis. Comput."},{"key":"4233_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2025.101324","volume":"12","author":"A Cardellicchio","year":"2025","unstructured":"Cardellicchio, A., Ren\u00f2, V., Cellini, F., Summerer, S., Petrozza, A., Milella, A.: Incremental learning with domain adaption for tomato plant phenotyping. Smart Agric. Technol. 12, 101324 (2025). https:\/\/doi.org\/10.1016\/j.atech.2025.101324","journal-title":"Smart Agric. Technol."},{"issue":"1","key":"4233_CR22","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/solar5010006","volume":"5","author":"R Khanam","year":"2025","unstructured":"Khanam, R., Asghar, T., Hussain, M.: Comparative performance evaluation of YOLOv5, YOLOv8, and YOLOv11 for solar panel defect detection. Solar 5(1), 6 (2025). https:\/\/doi.org\/10.3390\/solar5010006","journal-title":"Solar"},{"issue":"2","key":"4233_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/s24020395","volume":"24","author":"Y Li","year":"2024","unstructured":"Li, Y., Ma, C., Li, L., Wang, R., Liu, Z., Sun, Z.: Lightweight tunnel obstacle detection based on improved YOLOv5. Sensors 24(2), 395 (2024). https:\/\/doi.org\/10.3390\/s24020395","journal-title":"Sensors"},{"key":"4233_CR24","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks (2019), arXiv: arXiv:1709.01507. https:\/\/doi.org\/10.48550\/arXiv.1709.01507.","DOI":"10.48550\/arXiv.1709.01507"},{"key":"4233_CR25","doi-asserted-by":"publisher","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA: IEEE, June 2020, pp. 11531\u201311539. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"4233_CR26","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module (2018), arXiv: arXiv:1807.06521. https:\/\/doi.org\/10.48550\/arXiv.1807.06521","DOI":"10.48550\/arXiv.1807.06521"},{"key":"4233_CR27","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, July 2017. https:\/\/doi.org\/10.1109\/cvpr.2017.106","DOI":"10.1109\/cvpr.2017.106"},{"key":"4233_CR28","doi-asserted-by":"publisher","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation, (2018), arXiv: arXiv:1803.01534. https:\/\/doi.org\/10.48550\/arXiv.1803.01534","DOI":"10.48550\/arXiv.1803.01534"},{"key":"4233_CR29","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection, (2020), arXiv: arXiv:1911.09070. https:\/\/doi.org\/10.48550\/arXiv.1911.09070","DOI":"10.48550\/arXiv.1911.09070"},{"key":"4233_CR30","doi-asserted-by":"publisher","unstructured":"Ghiasi, G., Lin, T.-Y., Pang, R., Le, Q.V. :NAS-FPN: learning scalable feature pyramid architecture for object detection, (2019), arXiv: arXiv:1904.07392. https:\/\/doi.org\/10.48550\/arXiv.1904.07392","DOI":"10.48550\/arXiv.1904.07392"},{"key":"4233_CR31","doi-asserted-by":"publisher","unstructured":"Liu, S., Huang, D., Wang, Y., Learning spatial fusion for single-shot object detection, (2019), arXiv: arXiv:1911.09516. https:\/\/doi.org\/10.48550\/arXiv.1911.09516","DOI":"10.48550\/arXiv.1911.09516"},{"key":"4233_CR32","first-page":"11863","volume":"139","author":"L Yang","year":"2021","unstructured":"Yang, L., Zhang, R.Y., Li, L., et al.: Simam: A simple, parameter-free attention module for convolutional neural networks. Int. Conf. Mach. Learn. PMLR 139, 11863\u201311874 (2021). https:\/\/proceedings.mlr.press\/v139\/yang21o","journal-title":"Int. Conf. Mach. Learn. PMLR"},{"key":"4233_CR33","unstructured":"Park, J., Woo, S., Lee, J.Y., et al.: BAM: bottleneck attention module. (2018), arxiv preprint arXiv:1807.06514. https:\/\/arxiv.org\/abs\/1807.06514"},{"key":"4233_CR34","doi-asserted-by":"publisher","unstructured":"Liu, Y., Shao, Z., Hoffmann, N.: Global attention mechanism: retain information to enhance channel-spatial interactions, (2021), arXiv: arXiv:2112.05561. https:\/\/doi.org\/10.48550\/arXiv.2112.05561","DOI":"10.48550\/arXiv.2112.05561"},{"key":"4233_CR35","first-page":"10353","volume":"35","author":"Y Rao","year":"2022","unstructured":"Rao, Y., Zhao, W., Tang, Y., et al.: Hornet: Efficient high-order spatial interactions with recursive gated convolutions. Adv. Neural Inf. Process. Syst. 35, 10353\u201310366 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"14","key":"4233_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/app12147255","volume":"12","author":"H-K Jung","year":"2022","unstructured":"Jung, H.-K., Choi, G.-S.: Improved YOLOv5: efficient object detection using drone images under various conditions. Appl. Sci. 12(14), 7255 (2022). https:\/\/doi.org\/10.3390\/app12147255","journal-title":"Appl. Sci."},{"key":"4233_CR37","doi-asserted-by":"publisher","unstructured":"Li, C., et al.: YOLOv6 v3.0: a full-scale reloading, (2023), arXiv: arXiv:2301.05586. https:\/\/doi.org\/10.48550\/arXiv.2301.05586","DOI":"10.48550\/arXiv.2301.05586"},{"issue":"07","key":"4233_CR38","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. AAAI 34(07), 12993\u201313000 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6999","journal-title":"AAAI"},{"key":"4233_CR39","doi-asserted-by":"publisher","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA: IEEE, June 2019. https:\/\/doi.org\/10.1109\/cvpr.2019.00075","DOI":"10.1109\/cvpr.2019.00075"},{"issue":"8","key":"4233_CR40","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/tcyb.2021.3095305","volume":"52","author":"Z Zheng","year":"2022","unstructured":"Zheng, Z., et al.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybern. 52(8), 8574\u20138586 (2022). https:\/\/doi.org\/10.1109\/tcyb.2021.3095305","journal-title":"IEEE Trans. Cybern."},{"key":"4233_CR41","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y-F Zhang","year":"2022","unstructured":"Zhang, Y.-F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146\u2013157 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.07.042","journal-title":"Neurocomputing"},{"key":"4233_CR42","unstructured":"Gevorgyan, Z.: SIoU loss: more powerful learning for bounding box regression (2022). arxiv preprint arxiv:2205.12740"},{"key":"4233_CR43","first-page":"20230","volume":"34","author":"J He","year":"2021","unstructured":"He, J., Erfani, S., Ma, X., et al.: Alpha-IoU: a family of power intersection over union losses for bounding box regression. Adv. Neural Inf. Process. Syst. 34, 20230\u201320242 (2021). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/hash\/a8f15eda80c50adb0e71943adc8015cf-Abstract.html","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"4233_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.102894","volume":"106","author":"J Wu","year":"2019","unstructured":"Wu, J., Cai, N., Chen, W., Wang, H., Wang, G.: Automatic detection of hardhats worn by construction personnel: a deep learning approach and benchmark dataset. Autom. Constr. 106, 102894 (2019). https:\/\/doi.org\/10.1016\/j.autcon.2019.102894","journal-title":"Autom. Constr."},{"key":"4233_CR45","doi-asserted-by":"publisher","unstructured":"Redmon J., Farhadi, A.: YOLOv3: an incremental improvement, (2018), arXiv: arXiv:1804.02767. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"4233_CR46","doi-asserted-by":"publisher","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M.: YOLOv4: optimal speed and accuracy of object detection, (2020), arXiv: arXiv:2004.10934. https:\/\/doi.org\/10.48550\/arXiv.2004.10934.","DOI":"10.48550\/arXiv.2004.10934"},{"key":"4233_CR47","doi-asserted-by":"publisher","unstructured":"Cheng, H., et al.: An improved YOLOv8 algorithm model for detection of personal protective products in chemical plants. In: Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024), vol. 11, Haldorai, A., Singh, D., Kumar, A., Talpur, M. S. H., Djeddi, C. (eds.), in Atlantis Highlights in Intelligent Systems, vol. 11., pp. 487\u2013496. Atlantis Press International BV, Dordrecht (2024). https:\/\/doi.org\/10.2991\/978-94-6463-490-7_53","DOI":"10.2991\/978-94-6463-490-7_53"},{"key":"4233_CR48","doi-asserted-by":"publisher","unstructured":"Zhao, Y., et al.: DETRs beat YOLOs on real-time object detection (2024), arXiv: arXiv:2304.08069. https:\/\/doi.org\/10.48550\/arXiv.2304.08069","DOI":"10.48550\/arXiv.2304.08069"},{"key":"4233_CR49","doi-asserted-by":"publisher","unstructured":"Wang, A., et al.: YOLOv10: real-time end-to-end object detection (2024), arXiv: arXiv:2405.14458. https:\/\/doi.org\/10.48550\/arXiv.2405.14458","DOI":"10.48550\/arXiv.2405.14458"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04233-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04233-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04233-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:03:49Z","timestamp":1772629429000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04233-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4233"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04233-9","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]},"assertion":[{"value":"20 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"8"}}