{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:02:16Z","timestamp":1780020136288,"version":"3.53.1"},"reference-count":78,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116200","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:31:58Z","timestamp":1778859118000},"page":"116200","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["EfficientMedFormer: A directional decomposition approach for lightweight medical image classification"],"prefix":"10.1016","volume":"346","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0577-2436","authenticated-orcid":false,"given":"Seongchan","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0743-2386","authenticated-orcid":false,"given":"Jinbin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4113-5118","authenticated-orcid":false,"given":"Seunghyun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6595-6415","authenticated-orcid":false,"given":"Soonchul","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116200_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114468","article-title":"MIAFEx: An attention-based feature extraction method for medical image classification","volume":"330","author":"Ramos-Soto","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116200_b2","doi-asserted-by":"crossref","first-page":"24700","DOI":"10.1109\/ACCESS.2024.3365055","article-title":"A study on the implementation of temporal noise-robust methods for acquiring vital signs","volume":"12","author":"Park","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2026.116200_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113599","article-title":"Adaptive frequency-domain enhanced deep model driven by heterogeneous networks for medical image segmentation","volume":"319","author":"Liu","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"10.1016\/j.knosys.2026.116200_b4","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.1038\/s41467-021-26023-2","article-title":"Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams","volume":"12","author":"Shen","year":"2021","journal-title":"Nat. Commun."},{"issue":"2","key":"10.1016\/j.knosys.2026.116200_b5","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.knosys.2026.116200_b6","doi-asserted-by":"crossref","first-page":"217830","DOI":"10.1109\/ACCESS.2020.3040486","article-title":"3D CNN design for the classification of alzheimer\u2019s disease using brain MRI and PET","volume":"8","author":"Khagi","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2026.116200_b7","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.knosys.2026.116200_b8","series-title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","author":"Zhu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116200_b9","series-title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","author":"Ma","year":"2024"},{"key":"10.1016\/j.knosys.2026.116200_b10","series-title":"Medical image classification with kan-integrated transformers and dilated neighborhood attention","author":"Manzari","year":"2025"},{"key":"10.1016\/j.knosys.2026.116200_b11","series-title":"Dilated neighborhood attention transformer","author":"Hassani","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b12","series-title":"2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference","article-title":"1M parameters are enough? A lightweight CNN-based model for medical image segmentation","author":"Dinh","year":"2023"},{"key":"10.1016\/j.knosys.2026.116200_b13","doi-asserted-by":"crossref","first-page":"35932","DOI":"10.1109\/ACCESS.2022.3163711","article-title":"ELU-net: An efficient and lightweight U-net for medical image segmentation","volume":"10","author":"Deng","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2026.116200_b14","series-title":"2021 IEEE 34th International Symposium on Computer-Based Medical Systems","article-title":"Nanonet: Real-time polyp segmentation in video capsule endoscopy and colonoscopy","author":"Jha","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b15","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Emcad: efficient multi-scale convolutional attention decoding for medical image segmentation","author":"Rahman","year":"2024"},{"key":"10.1016\/j.knosys.2026.116200_b16","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision","article-title":"CAMS: convolution and attention-free mamba-based cardiac image segmentation","author":"Khan","year":"2025"},{"key":"10.1016\/j.knosys.2026.116200_b17","series-title":"Medmamba: Vision mamba for medical image classification","author":"Yue","year":"2024"},{"issue":"6","key":"10.1016\/j.knosys.2026.116200_b18","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1109\/TMI.2021.3140140","article-title":"Robust medical image classification from noisy labeled data with global and local representation guided co-training","volume":"41","author":"Xue","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.knosys.2026.116200_b19","doi-asserted-by":"crossref","first-page":"6384","DOI":"10.1007\/s00330-021-07709-z","article-title":"Deep learning\u2013based metal artefact reduction in PET\/CT imaging","volume":"31","author":"Arabi","year":"2021","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.knosys.2026.116200_b20","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.knosys.2026.116200_b21","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.knosys.2026.116200_b22","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Non-local neural networks","author":"Wang","year":"2018"},{"key":"10.1016\/j.knosys.2026.116200_b23","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","article-title":"Unetr: Transformers for 3d medical image segmentation","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b24","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b25","series-title":"Efficiently modeling long sequences with structured state spaces","author":"Gu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b26","first-page":"103031","article-title":"Vmamba: Visual state space model","volume":"37","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116200_b27","series-title":"2023 3rd International Conference on Advanced Research in Computing","article-title":"Melanoma skin cancer classification with explainability","author":"Gamage","year":"2023"},{"key":"10.1016\/j.knosys.2026.116200_b28","series-title":"2021 International Conference on Decision Aid Sciences and Application","article-title":"MobileNetV2 based chest X-rays classification","author":"Kolonne","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b29","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b30","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Cmt: Convolutional neural networks meet vision transformers","author":"Guo","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b31","first-page":"3965","article-title":"Coatnet: Marrying convolution and attention for all data sizes","volume":"34","author":"Dai","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116200_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106791","article-title":"MedViT: a robust vision transformer for generalized medical image classification","volume":"157","author":"Manzari","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.116200_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107348","article-title":"MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation","volume":"230","author":"Yang","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"10.1016\/j.knosys.2026.116200_b34","article-title":"ParMamba: A parallel architecture using CNN and mamba for brain tumor classification","volume":"142","author":"Su","year":"2025","journal-title":"Comput. Model. Eng. Sci. (CMES)"},{"key":"10.1016\/j.knosys.2026.116200_b35","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"EfficientViT: memory efficient vision transformer with cascaded group attention","author":"Liu","year":"2023"},{"key":"10.1016\/j.knosys.2026.116200_b36","series-title":"Lightm-UNet: Mamba assists in lightweight unet for medical image segmentation","author":"Liao","year":"2024"},{"key":"10.1016\/j.knosys.2026.116200_b37","series-title":"European Conference on Computer Vision","article-title":"MobileNetV4: universal models for the mobile ecosystem","author":"Qin","year":"2024"},{"key":"10.1016\/j.knosys.2026.116200_b38","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Mobile-former: Bridging mobilenet and transformer","author":"Chen","year":"2022"},{"issue":"1","key":"10.1016\/j.knosys.2026.116200_b39","doi-asserted-by":"crossref","first-page":"4155","DOI":"10.1109\/TCE.2023.3341852","article-title":"Acfusion: Infrared and visible image fusion based on self-attention and convolution with enhanced information extraction","volume":"70","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10.1016\/j.knosys.2026.116200_b40","article-title":"Vm-unet: Vision mamba unet for medical image segmentation","author":"Ruan","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.knosys.2026.116200_b41","series-title":"MS-UMamba: An improved vision mamba unet for fetal abdominal medical image segmentation","author":"Xu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116200_b42","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"ECA-net: Efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2020"},{"key":"10.1016\/j.knosys.2026.116200_b43","series-title":"Proceedings of the European Conference on Computer Vision","article-title":"Cbam: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.knosys.2026.116200_b44","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.knosys.2026.116200_b45","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"A convnet for the 2020s","author":"Liu","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b46","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Scaling up your kernels to 31x31: Revisiting large kernel design in cnns","author":"Ding","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b47","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Ccnet: Criss-cross attention for semantic segmentation","author":"Huang","year":"2019"},{"key":"10.1016\/j.knosys.2026.116200_b48","series-title":"Polarized self-attention: Towards high-quality pixel-wise regression","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b49","series-title":"European conference on computer vision","article-title":"Axial-deeplab: Stand-alone axial-attention for panoptic segmentation","author":"Wang","year":"2020"},{"key":"10.1016\/j.knosys.2026.116200_b50","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Strip pooling: Rethinking spatial pooling for scene parsing","author":"Hou","year":"2020"},{"key":"10.1016\/j.knosys.2026.116200_b51","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"On the integration of self-attention and convolution","author":"Pan","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b52","series-title":"MobileNets: efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.knosys.2026.116200_b53","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.knosys.2026.116200_b54","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Neighborhood attention transformer","author":"Hassani","year":"2023"},{"issue":"1","key":"10.1016\/j.knosys.2026.116200_b55","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","article-title":"Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification","volume":"10","author":"Yang","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.knosys.2026.116200_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2020.106221","article-title":"PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones","volume":"32","author":"Pacheco","year":"2020","journal-title":"Data Brief."},{"key":"10.1016\/j.knosys.2026.116200_b57","series-title":"Proceedings of the 8th ACM on Multimedia Systems Conference","article-title":"Kvasir: a multi-class image dataset for computer aided gastrointestinal disease detection","author":"Pogorelov","year":"2017"},{"key":"10.1016\/j.knosys.2026.116200_b58","unstructured":"M Nickparvar, Brain Tumor MRI Dataset, Kaggle, 2021. http:\/\/dx.doi.org\/10.34740\/KAGGLE\/DSV\/2645886."},{"key":"10.1016\/j.knosys.2026.116200_b59","unstructured":"J. Cheng, brain tumor dataset, figshare, 2017. http:\/\/dx.doi.org\/10.6084\/m9.figshare.1512427.v8."},{"key":"10.1016\/j.knosys.2026.116200_b60","unstructured":"S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, S. Kanchan, Brain Tumor Classification (MRI), Kaggle, 2025. http:\/\/dx.doi.org\/10.34740\/KAGGLE\/DSV\/12745533."},{"issue":"1","key":"10.1016\/j.knosys.2026.116200_b61","doi-asserted-by":"crossref","first-page":"7669","DOI":"10.1038\/s41598-025-92156-9","article-title":"Rethinking model prototyping through the MedMNIST+ dataset collection","volume":"15","author":"Doerrich","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2026.116200_b62","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.knosys.2026.116200_b63","series-title":"International Conference on Machine Learning","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.knosys.2026.116200_b64","series-title":"European Conference on Computer Vision","article-title":"EdgeNeXt: efficiently amalgamated CNN-transformer architecture for mobile vision applications","author":"Maaz","year":"2022"},{"key":"10.1016\/j.knosys.2026.116200_b65","series-title":"2025 3rd World Conference on Communication & Computing","first-page":"1","article-title":"MobileViT-based deep learning model for brain tumor classification: performance and scalability analysis","author":"Shashank","year":"2025"},{"key":"10.1016\/j.knosys.2026.116200_b66","series-title":"International Conference on Machine Learning","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105171","article-title":"Eva-02: a visual representation for neon genesis","volume":"149","author":"Fang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.knosys.2026.116200_b68","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Emerging properties in self-supervised vision transformers","author":"Caron","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b69","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.knosys.2026.116200_b70","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b71","series-title":"EfficientNetV2: smaller models and faster training","author":"Tan","year":"2021"},{"issue":"2","key":"10.1016\/j.knosys.2026.116200_b72","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/TPAMI.2023.3329173","article-title":"MetaFormer baselines for vision","volume":"46","author":"Yu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.knosys.2026.116200_b73","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"PVT v2: improved baselines with pyramid vision transformer","volume":"8","author":"Wang","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"10.1016\/j.knosys.2026.116200_b74","series-title":"International Conference on Machine Learning","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"10.1016\/j.knosys.2026.116200_b75","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.knosys.2026.116200_b76","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"issue":"1","key":"10.1016\/j.knosys.2026.116200_b77","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1038\/s41598-025-86752-y","article-title":"Dilated SE-DenseNet for brain tumor MRI classification","volume":"15","author":"Mao","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2026.116200_b78","series-title":"Proceedings of the IEEE International Conference on Computer Vision","article-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126009263?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126009263?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:09:21Z","timestamp":1780016961000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126009263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":78,"alternative-id":["S0950705126009263"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116200","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EfficientMedFormer: A directional decomposition approach for lightweight medical image classification","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116200","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"116200"}}