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Imaging"],"abstract":"<jats:p>Current deep learning-based dense target detection models face dual challenges in industrial scenarios: high computational complexity leading to insufficient inference efficiency on mobile devices, and missed\/false detections caused by dense small targets, high inter-class similarity, and complex background interference. To address these issues, this paper proposes DBA-YOLO, a lightweight model based on YOLOv10, which significantly reduces computational complexity through model compression and algorithm optimization while maintaining high accuracy. Key improvements include the following: (1) a C2f PA module for enhanced feature extraction, (2) a parameter-refined BIMAFPN neck structure to improve small target detection, and (3) a DyDHead module integrating scale, space, and task awareness for spatial feature weighting. To validate DBA-YOLO, we constructed a real-world dataset from cigarette package images. Experiments on SKU-110K and our dataset show that DBA-YOLO achieves 91.3% detection accuracy (1.4% higher than baseline), with mAP and mAP75 improvements of 2\u20133%. Additionally, the model reduces parameters by 3.6%, balancing efficiency and performance for resource-constrained devices.<\/jats:p>","DOI":"10.3390\/jimaging11100345","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T11:42:49Z","timestamp":1759750969000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DBA-YOLO: A Dense Target Detection Model Based on Lightweight Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6238-9302","authenticated-orcid":false,"given":"Zhiyong","family":"He","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2306-0156","authenticated-orcid":false,"given":"Jiahong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3967-6242","authenticated-orcid":false,"given":"Hongtian","family":"Ning","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3607-9841","authenticated-orcid":false,"given":"Chengxuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5712-589X","authenticated-orcid":false,"given":"Qiang","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Engineering and Design, Hunan Normal University, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liang, L., Ma, H., Zhao, L., Xie, X., Hua, C., Zhang, M., and Zhang, Y. (2024). Vehicle detection algorithms for autonomous driving: A review. Sensors, 24.","DOI":"10.3390\/s24103088"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"EL Fadel, N. (2025). Facial Recognition Algorithms: A Systematic Literature Review. J. Imaging, 11.","DOI":"10.3390\/jimaging11020058"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"AlKendi, W., Gechter, F., Heyberger, L., and Guyeux, C. (2024). Advancements and challenges in handwritten text recognition: A comprehensive survey. J. Imaging, 10.","DOI":"10.3390\/jimaging10010018"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.32604\/csse.2022.023221","article-title":"Planetscope nanosatellites image classification using machine learning","volume":"42","author":"Haq","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., and Hassner, T. (2019, January 16\u201320). Precise detection in densely packed scenes. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00537"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s12524-020-01231-3","article-title":"Deep learning based supervised image classification using UAV images for forest areas classification","volume":"49","author":"Haq","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ma, Z., Liu, D., Cui, Z., and Zhao, Y. (2023, January 18\u201322). AdaptCD: An adaptive target region-based commodity detection system. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00580"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"121983","DOI":"10.1109\/ACCESS.2024.3390049","article-title":"Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities","volume":"12","author":"Alsubai","year":"2024","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"837","DOI":"10.32604\/csse.2022.023016","article-title":"CNN based automated weed detection system using UAV imagery","volume":"42","author":"Haq","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 24\u201327). CNN features off-the-shelf: An astounding baseline for recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103514","DOI":"10.1016\/j.dsp.2022.103514","article-title":"A survey of modern deep learning based object detection models","volume":"126","author":"Zaidi","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201310). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_15","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_18","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Varghese, R., and Sambath, M. (2024, January 18\u201319). Yolov8: A novel object detection algorithm with enhanced performance and robustness. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India.","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"ref_20","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_21","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). Yolov12: Attention-centric real-time object detectors. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_23","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_24","unstructured":"Lv, W., Zhao, Y., Chang, Q., Huang, K., Wang, G., and Liu, Y. (2024). Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, S., Lu, Z., Cun, X., Yu, Y., Zhou, X., and Shen, X. (2025, January 11\u201315). Deim: Detr with improved matching for fast convergence. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01412"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chu, X., Zheng, A., Zhang, X., and Sun, J. (2020, January 14\u201319). Detection in crowded scenes: One proposal, multiple predictions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01223"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, J., Song, L., Li, Z., Sun, H., Sun, J., and Zheng, N. (2021, January 19\u201325). End-to-end object detection with fully convolutional network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.01559"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, C., Huang, Z., and Wang, N. (2022, January 18\u201324). QueryDet: Cascaded sparse query for accelerating high-resolution small object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01330"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, X., Wang, J., Pang, J., Lyu, C., Zhang, W., Luo, P., and Chen, K. (2023, January 18\u201322). Dense distinct query for end-to-end object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00708"},{"key":"ref_30","unstructured":"Lin, Z., Wang, Y., Zhang, J., and Chu, X. (2024, January 16\u201322). Dynamicdet: A unified dynamic architecture for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018;, January 21\u201326). Path Aggregation Network for Instance Segmentation. Proceedings of the CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the CVPR, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, C., Fan, B., Zhang, Q., Liu, X., Zhang, M., and Lu, H. (2020, January 14\u201319). AugFPN: Improving Multi-scale Feature Learning for Object Detection. Proceedings of the CVPR, Online.","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"30685","DOI":"10.1007\/s11042-022-11940-1","article-title":"CE-FPN: Enhancing Channel Information for Object Detection","volume":"81","author":"Luo","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, G., Lei, J., Zhu, Z., Cheng, S., Feng, Z., and Liang, R. (2023, January 5\u20138). AFPN: Asymptotic Feature Pyramid Network for Object Detection. Proceedings of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria.","DOI":"10.1109\/SMC53992.2023.10394415"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/TCSVT.2023.3286896","article-title":"High-Resolution Feature Pyramid Network for Small Object Detection","volume":"34","author":"Chen","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_38","unstructured":"Shi, Z., Hu, J., Ren, J., Ye, H., Yuan, X., Ouyang, Y., He, J., Ji, B., and Guo, J. (March, January 27). High Frequency and Spatial Perception Feature Pyramid Network for Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_39","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dai, Z., Cai, Q., Lin, Y., Chen, Y., Ding, M., Xie, E., Zhang, W., Hu, H., and Dai, J. (2021, January 19\u201325). Dynamic Head: Unifying Object Detection Heads with Attentions. Proceedings of the CVPR, Online.","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"ref_43","unstructured":"Zhang, T., Cheng, C., Lu, C., Li, K., Yang, X., Li, G., and Zhang, L. (2021, January 10\u201317). TOOD: Task-aligned One-stage Object Detection. Proceedings of the ICCV, Seoul, Republic of Korea."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_45","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020, January 6\u201312). Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes. Proceedings of the NeurIPS, Online."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the ICCV, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","article-title":"Detection and tracking meet drones challenge","volume":"44","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","unstructured":"Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., and Wang, X. (2022). Yolo-facev2: A scale and occlusion aware face detector. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, X., Hu, D., Cheng, Y., Chen, S., and Xiang, J. (2025). EDT-YOLOv8n-Based Lightweight Detection of Kiwifruit in Complex Environments. Electronics, 14.","DOI":"10.3390\/electronics14010147"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1364\/JOSAA.533407","article-title":"ECM-YOLO: A real-time detection method of steel surface defects based on multiscale convolution","volume":"41","author":"Yan","year":"2024","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, X., Song, L., Zhang, Y., Rong, X., and Wang, Y. (2024). Research on a train safety driving method based on fusion of an incremental clustering algorithm and lightweight shared convolution. Sensors, 24.","DOI":"10.3390\/s24154951"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.isprsjprs.2024.01.004","article-title":"ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning","volume":"208","author":"Dong","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chen, J., Mai, H., Luo, L., Chen, X., and Wu, K. (2021, January 19\u201322). Effective feature fusion network in BIFPN for small object detection. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506347"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11554-024-01436-6","article-title":"Slim-neck by GSConv: A lightweight-design for real-time detector architectures","volume":"21","author":"Li","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_56","first-page":"51094","article-title":"Gold-YOLO: Efficient object detector via gather-and-distribute mechanism","volume":"36","author":"Wang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"105057","DOI":"10.1016\/j.imavis.2024.105057","article-title":"ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation","volume":"147","author":"Kang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Du, Z., Hu, Z., Zhao, G., Jin, Y., and Ma, H. (2024). Cross-layer feature pyramid transformer for small object detection in aerial images. arXiv.","DOI":"10.1109\/TGRS.2025.3572706"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kang, M., Ting, C.M., Ting, F.F., and Phan, R.C.W. (2023, January 8\u201312). RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, Canada.","DOI":"10.1007\/978-3-031-43901-8_57"},{"key":"ref_60","unstructured":"Xu, X., Jiang, Y., Chen, W., Huang, Y., Zhang, Y., and Sun, X. (2022). Damo-yolo: A report on real-time object detection design. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ye, R., Shao, G., He, Y., Gao, Q., and Li, T. (2024). YOLOv8-RMDA: Lightweight YOLOv8 network for early detection of small target diseases in tea. Sensors, 24.","DOI":"10.3390\/s24092896"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., and Peng, Y. (2024). Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases. Comput. Biol. Med., 170.","DOI":"10.1016\/j.compbiomed.2024.107917"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/10\/345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T12:23:08Z","timestamp":1759753388000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/10\/345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"references-count":62,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["jimaging11100345"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11100345","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]}}}