{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:09:57Z","timestamp":1790014197376,"version":"4.0.1"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>Radiation-shielding materials are critically important for protecting human health in nuclear energy, medical imaging, and radiotherapy applications. Due to the toxicity and environmental disadvantages of traditional lead-based materials, boron-doped glasses represent a promising alternative because of their radiation-shielding characteristics, optical transparency, and relatively low toxicity. This study evaluated whether a systematically selected artificial neural network (ANN) provides a meaningful predictive advantage over multiple linear regression (MLR) and support vector regression (SVR) for broad-spectrum linear attenuation coefficient (LAC) estimation while quantifying energy-dependent prediction error, input-importance uncertainty, and transferability limits. After removing one duplicated 0.0221 MeV record per glass, the final Phy-X\/PSD-derived computational dataset comprised 546 observations from six glass compositions evaluated at 91 unique photon-energy points over 0.015\u201315 MeV. The 2\u201310\u20131\/tansig ANN was selected using photon-energy-grouped cross-validation and the one-standard-error rule with parsimony. It achieved pooled out-of-fold RMSE = 0.020453 cm\u22121, MAE = 0.004061 cm\u22121, R2 = 0.999915, and MAPE = 0.854%, outperforming MLR and RBF-SVR under the common validation protocol. Perturbation analysis identified photon energy as the dominant predictive input (95.75%), while B2O3 concentration, interpreted as a compositional descriptor of the S1\u2013S6 series, contributed 4.25% model-specific importance. Repeated-split analysis supported strong within-domain interpolation for most partitions, whereas leave-one-energy-interval-out testing showed poor boundary-energy extrapolation and leave-one-composition-out testing revealed strongly nonuniform composition transferability. The ANN should therefore be used only as a preliminary computational screening and decision-support tool within the represented domain, with independent experimental validation required before engineering or safety-critical use.<\/jats:p>","DOI":"10.3390\/ma19183988","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:23:17Z","timestamp":1790011397000},"page":"3988","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Radiation-Shielding Performance of Boron-Doped Glasses Using Artificial Neural Networks and Statistical Analysis"],"prefix":"10.3390","volume":"19","author":[{"given":"Bekir","family":"Oruncak","sequence":"first","affiliation":[{"name":"Physics Department, Science and Art Faculty, Afyon Kocatepe University, Afyonkarahisar 03200, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Seher","family":"Polat","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, Sakarya University, Sakarya 54187, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Kerem","family":"Hepdeniz","sequence":"additional","affiliation":[{"name":"Bucak Emin G\u00fclmez Vocational School of Technical Sciences, Burdur Mehmet Akif Ersoy University, Burdur 15300, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6970-6939","authenticated-orcid":false,"given":"Hatice Banu","family":"Keskinkaya","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Faculty of Science, Necmettin Erbakan University, Konya 42090, Turkey"},{"name":"Application and Research Center for Medicinal and Cosmetic Plants, Necmettin Erbakan University, Konya 42090, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.jmrt.2022.02.074","article-title":"Heavy Metal Oxide (HMO) glasses as an effective member of glass shield family: A comprehensive characterization on gamma ray shielding properties of various structures","volume":"18","author":"Tekin","year":"2022","journal-title":"J. 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