{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:15:55Z","timestamp":1772252155556,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Haze significantly reduces visibility in critical applications such as autonomous driving, surveillance, and firefighting, making its removal essential for safety and reliability. Motivated by the limited robustness of the existing methods under non-uniform haze conditions, this study introduces a novel regression-based dehazing model that simultaneously incorporates the atmospheric light constant, transmission map, and scattering coefficient for improved restoration. Instead of relying on complex deep networks, the model leverages brightness\u2013saturation cues and regression-driven scattering estimation with localized haze detection to reconstruct clearer images efficiently. Evaluated on the RESIDE dataset, the approach consistently surpasses state-of-the-art techniques including Dark Channel Prior, AOD-Net, FFA-Net, and Single U-Net, achieving SSIM = 0.99, PSNR = 22.25 dB, VIF = 1.08, and the lowest processing time of 0.038 s, demonstrating both accuracy and practicality for real-world deployment.<\/jats:p>","DOI":"10.3390\/bdcc9110282","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T10:35:31Z","timestamp":1762770931000},"page":"282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Pixel-Dehaze: Deciphering Dehazing Through Regression-Based Depth and Scattering Estimation"],"prefix":"10.3390","volume":"9","author":[{"given":"Vaibhav","family":"Baldeva","sequence":"first","affiliation":[{"name":"Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala 147004, Punjab, India"}]},{"given":"Vishakha","family":"Sharma","sequence":"additional","affiliation":[{"name":"Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala 147004, Punjab, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6922-3614","authenticated-orcid":false,"given":"Satakshi","family":"Verma","sequence":"additional","affiliation":[{"name":"Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala 147004, Punjab, India"}]},{"given":"Priya","family":"Kansal","sequence":"additional","affiliation":[{"name":"Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala 147004, Punjab, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0748-4039","authenticated-orcid":false,"given":"Sachin","family":"Kansal","sequence":"additional","affiliation":[{"name":"Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala 147004, Punjab, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2499-6039","authenticated-orcid":false,"given":"Jyotindra","family":"Narayan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Indian Institute of Technology, Patna 801106, Bihar, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, J., Chen, Y., Wang, W., Yan, P., Liu, H., Yang, S., Hu, Z., and Lelieveld, J. (2010). Strong air pollution causes widespread haze-clouds over China. J. Geophys. Res. Atmos., 115.","DOI":"10.1029\/2009JD013065"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s00024-003-8771-x","article-title":"Chemistry of forest fires and regional haze with emphasis on Southeast Asia","volume":"160","author":"Radojevic","year":"2003","journal-title":"Pure Appl. Geophys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1097\/OPX.0000000000000355","article-title":"Visibility through atmospheric haze and its relation to macular pigment","volume":"91","author":"Fletcher","year":"2014","journal-title":"Optom. Vis. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, X., Gao, L., Li, P., and Hao, J. (2013, January 16\u201318). Image dehazing using dark channels with global transmission. Proceedings of the 2013 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China.","DOI":"10.1109\/CISP.2013.6744002"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tang, S.J., Zeng, H.X., and Lee, Y.H. (2018, January 24\u201327). Fast airlight estimation algorithm in dark channel prior for image dehazing applications. Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA,.","DOI":"10.23919\/ELINFOCOM.2018.8330695"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Megha, P.R., and Sreeni, K.G. (2021, January 18\u201319). Dark Channel Prior based Image Dehazing with Contrast Enhancement. Proceedings of the 2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS), Kollam, India.","DOI":"10.1109\/ICMSS53060.2021.9673605"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, Y.F., Liaw, C.H., and Lee, Y.H. (2022, January 6\u20138). Down-Sampling Dark Channel Prior of Airlight Estimation for Low Complexity Image Dehazing Chip Design. Proceedings of the 2022 IEEE International Conference on Consumer Electronics\u2014Taiwan, Taipei, Taiwan.","DOI":"10.1109\/ICCE-Taiwan55306.2022.9869163"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hsieh, C.H., Chen, J.Y., and Zhao, Q. (2018, January 7\u201310). A Modified DCP Based Dehazing Algorithm. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00307"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Balaji, V.D., Kumar, A.E., Shanmuganathan, S., and Devi, S.P. (2021, January 2\u20134). Single Image Dehazing via Transmission Map Estimation using Deep Neural Networks. Proceedings of the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA52323.2021.9676125"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, W., Yuan, X., Wu, X., Liu, Y., and Ghanbarzadeh, S. (2016, January 25\u201328). An efficient method for image dehazing. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532757"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ngo, D., Lee, S., and Kang, B. (2020). Robust single-image haze removal using optimal transmission map and adaptive atmospheric light. Remote Sens., 12.","DOI":"10.3390\/rs12142233"},{"key":"ref_12","unstructured":"Lai, Y.S., Chen, Y.L., and Hsu, C.T. (2012, January 11\u201315). Single image dehazing with optimal transmission map. Proceedings of the Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shu, Q., Wu, C., Xiao, Z., and Liu, R.W. (2019, January 22\u201325). Variational Regularized Transmission Refinement for Image Dehazing. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803256"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wei, P., Wang, X., Wang, L., and Xiang, J. (2021, January 10\u201315). SIDGAN: Single Image Dehazing without Paired Supervision. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413155"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, C., Liu, G., Pang, Z., Xiang, P., and Pu, W. (2023, January 11\u201313). A Lightweight Image Dehazing Algorithm Based on Deep Learning. Proceedings of the 2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), Mianyang, China.","DOI":"10.1109\/RAIIC59453.2023.10281130"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"70160","DOI":"10.1109\/ACCESS.2024.3402084","article-title":"IDACC: Image Dehazing Avoiding Color Cast Using a Novel Atmospheric Scattering Model","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bao, H., Zhang, D., and Zhao, X. (2018, January 14\u201316). A single image dehazing method based on sky recognition and average saturation prior. Proceedings of the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.","DOI":"10.1109\/ITOEC.2018.8740377"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.1109\/TIP.2015.2446191","article-title":"A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior","volume":"24","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yadav, S.K., and Sarawadekar, K. (2019, January 17\u201320). Single Image Dehazing using Adaptive Gamma Correction Method. Proceedings of the TENCON 2019\u20132019 IEEE Region 10 Conference (TENCON), Kochi, India.","DOI":"10.1109\/TENCON.2019.8929383"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9043","DOI":"10.1109\/TIP.2021.3122088","article-title":"IDRLP: Image Dehazing Using Region Line Prior","volume":"30","author":"Ju","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TMM.2017.2652069","article-title":"Fast Image Dehazing Method Based on Linear Transformation","volume":"19","author":"Wang","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, B., Lai, Y., Wu, C., and Liu, Y. (2018, January 20\u201324). Fast Single Image Dehazing via Positive Correlation. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546251"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, B., Zhang, W., and Lu, M. (2018, January 12\u201314). Multiple Linear Regression Haze-Removal Model Based on Dark Channel Prior. Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI46756.2018.00066"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hassan, H., Luo, B., Xin, Q., Abbasi, R., and Ahmad, W. (2019, January 24\u201326). Single Image Dehazing from Repeated Averaging Filters. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785601"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, W., Ji, T., Wu, X., and Feng, L. (2018, January 18\u201320). Gray projection for single image dehazing. Proceedings of the 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Nanjing, China.","DOI":"10.1109\/YAC.2018.8406545"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/TMM.2018.2871955","article-title":"An Iterative Image Dehazing Method with Polarization","volume":"21","author":"Shen","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TIP.2022.3140609","article-title":"Self-Guided Image Dehazing Using Progressive Feature Fusion","volume":"31","author":"Bai","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Du, Y., and Li, X. (2018, January 18\u201322). Recursive Deep Residual Learning for Single Image Dehazing. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00116"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, K., Xu, N., and Li, Y. (2017, January 10\u201313). Quantitative evaluation for dehazing algorithms on synthetic outdoor hazy dataset. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305081"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yaz\u0131c\u0131, B., \u00c7imtay, Y., and \u00c7etinkaya, B. (November, January 31). A New Hyperspectral Multi-Level Synthetic Hazy Image Dataset for Benchmark of Dehazing Methods. Proceedings of the 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Athens, Greece.","DOI":"10.1109\/WHISPERS61460.2023.10430977"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Husain, N.A., Mohd Rahim, M.S., and Chaudhry, H. (2021, January 5\u20137). Different Haze Image Conditions for Single Image Dehazing Method. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA.","DOI":"10.1109\/SSCI50451.2021.9659889"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gao, Z., and Bai, Y. (2016, January 7\u20138). Single image haze removal algorithm using pixel-based airlight constraints. Proceedings of the 2016 22nd International Conference on Automation and Computing (ICAC), Colchester, UK.","DOI":"10.1109\/IConAC.2016.7604930"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"P, V., K S, A., Shetty, L., T M, K., and S S, S. (2023, January 3\u20137). Non Homogeneous Realistic Single Image Dehazing. Proceedings of the 2023 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Waikoloa, HI, USA.","DOI":"10.1109\/WACVW58289.2023.00061"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gao, S., Zhu, J., and Xi, H. (2021, January 5\u20139). Attention-Based Encoder-Decoder Network For Single Image Dehazing. Proceedings of the 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shenzhen, China.","DOI":"10.1109\/ICMEW53276.2021.9455979"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, L.Y., Yin, J.L., Chen, B.H., and Ye, S.Z. (2019, January 22\u201325). Towards Unsupervised Single Image Dehazing with Deep Learning. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803316"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Del Gallego, N.P., Ilao, J., Cordel, M., and Ruiz, C. (2022). A new approach for training a physics-based dehazing network using synthetic images. Signal Process., 199.","DOI":"10.1016\/j.sigpro.2022.108631"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ding, X., Wang, Y., Zhang, J., and Fu, X. (2017, January 19\u201322). Underwater image dehaze using scene depth estimation with adaptive color correction. Proceedings of the OCEANS 2017\u2014Aberdeen, Aberdeen, UK.","DOI":"10.1109\/OCEANSE.2017.8084665"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, J., Bryson, M., Williams, S.B., and Sanz, P.J. (2020). Recovering Depth from Still Images for Underwater Dehazing Using Deep Learning. Sensors, 20.","DOI":"10.3390\/s20164580"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/LSP.2022.3147434","article-title":"Unpaired Image Dehazing with Physical-Guided Restoration and Depth-Guided Refinement","volume":"29","author":"Chen","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, R.W., Xiong, S., and Wu, H. (2018, January 15\u201320). A Second-Order Variational Framework for Joint Depth Map Estimation and Image Dehazing. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462394"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dobre-Baron, R., and Ancu\u0163i, C. (2024, January 7\u20138). Dehazing CNNs Loss Functions Analysis. Proceedings of the 2024 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania.","DOI":"10.1109\/ISETC63109.2024.10797441"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chang, K.Y., Li, K.L., Sheu, M.H., and Wang, S.H. (2024, January 5\u20138). DMCGF Dehazing Neural Network Design for Edge-AI Implementation. Proceedings of the 2024 IEEE Cyber Science and Technology Congress (CyberSciTech), Boracay Island, Philippines.","DOI":"10.1109\/CyberSciTech64112.2024.00067"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jin, J.Y., Cui, Y.N., Ren, J., Lv, Y.X., and Hu, Z.J. (2024, January 8\u201312). Dust Weather Image Clarity Algorithm Based on Color Adjustment and Dark Channel Dehazing. Proceedings of the 2024 International Conference on Cyber-Physical Social Intelligence (ICCSI), Doha, Qatar.","DOI":"10.1109\/ICCSI62669.2024.10799408"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, D. (2024, January 21\u201323). A Dehazing Method Based on Fog Density Fusion of Dark and Bright Channel Images. Proceedings of the 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS62405.2024.10792686"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2024, January 21\u201323). ZRD-Net: A Zero-shot Low-Light Image Dehazing Network. Proceedings of the 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS62405.2024.10792704"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zheng, T., Xu, T., Li, X., Zhao, X., Zhao, F., and Zhang, Y. (2024, January 11\u201313). Improved AOD-Net Dehazing Algorithm for Target Image. Proceedings of the 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC), Guangzhou, China.","DOI":"10.1109\/ICCEIC64099.2024.10775918"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hafidh, F., Shidik, G.F., Syukur, A., Andono, P.N., and Soeleman, M.A. (2024, January 21\u201322). Advanced Dehazing of Single Satellite Imagery Using Enhanced Dark Channel Prior and Refined Transmission. Proceedings of the 2024 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia.","DOI":"10.1109\/iSemantic63362.2024.10762310"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/TIP.2019.2934360","article-title":"RYF-Net: Deep Fusion Network for Single Image Haze Removal","volume":"29","author":"Dudhane","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","unstructured":"Jain, A. (2025). Feature Fusion Attention Network with CycleGAN for Image Dehazing, De-Snowing and De-Raining. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111385","DOI":"10.1016\/j.engappai.2025.111385","article-title":"UTCR-Dehaze: U-Net and transformer-based cycle-consistent generative adversarial network for unpaired remote sensing image dehazing","volume":"158","author":"Li","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"62600","DOI":"10.1109\/ACCESS.2025.3558506","article-title":"Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image Enhancement","volume":"13","year":"2025","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","article-title":"Benchmarking Single-Image Dehazing and Beyond","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_53","unstructured":"Li, B., Ren, W., and Wang, Z. (2025, September 20). RESIDE-Standard: Single Image Dehazing Benchmark Dataset. Available online: https:\/\/sites.google.com\/view\/reside-dehaze-datasets\/reside-standard."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ancuti, C., Ancuti, C.O., and De Vleeschouwer, C. (2016, January 25\u201328). D-hazy: A dataset to evaluate quantitatively dehazing algorithms. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532754"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ding, L., and Sharma, G. (2017, January 17\u201320). Hazerd: An outdoor scene dataset and benchmark for single image dehazing. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296874"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/11\/282\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T05:16:50Z","timestamp":1762924610000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/11\/282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,8]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["bdcc9110282"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9110282","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,8]]}}}