{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:11:14Z","timestamp":1743016274795,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":12,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970186"},{"type":"electronic","value":"9789819970193"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-7019-3_9","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"92-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Driven Reactor Pressure Vessel Embrittlement Prediction Model"],"prefix":"10.1007","author":[{"given":"Pin","family":"Jin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haopeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingti","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengcao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Brillaud, C., Hedin, F., Houssin, B.: A comparison between French surveillance program results and predictions of irradiation embrittlement. In: Stoller, R.E., Garner, F.A., Henager, C.H., Iagata, N. (eds.) Effects of Radiation on Materials: 13th International Symposium, ASTM STP 956, Philadelphia, PA, pp. 420\u2013447. American Society for Testing and Materials (1987)","DOI":"10.1520\/STP25666S"},{"issue":"1\u20133","key":"9_CR2","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.jnucmat.2012.09.012","volume":"433","author":"ED Eason","year":"2013","unstructured":"Eason, E.D., Odette, G.R., Nanstad, R.K., et al.: A physically-based correlation of irradiation-induced transition temperature shifts for RPV steels. J. Nucl. Mater. 433(1\u20133), 240\u2013254 (2013)","journal-title":"J. Nucl. Mater."},{"issue":"2","key":"9_CR3","doi-asserted-by":"publisher","first-page":"186","DOI":"10.3390\/met12020186","volume":"12","author":"D Ferre\u00f1o","year":"2022","unstructured":"Ferre\u00f1o, D., Serrano, M., Kirk, M., et al.: Prediction of the transition-temperature shift using machine learning algorithms and the plotter database. Metals 12(2), 186 (2022)","journal-title":"Metals"},{"issue":"6","key":"9_CR4","first-page":"92","volume":"41","author":"K Jing","year":"2020","unstructured":"Jing, K., Kai, S., Xiaoxi, M., et al.: Research on prediction model of irradiation embrittlement of RPV materials based on artificial neural network. Nucl. Power Eng. 41(6), 92\u201395 (2020)","journal-title":"Nucl. Power Eng."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Kirk, M.: Summary of work to develop the transition temperature shift equation used in ASTM standard guide e900\u201315. In: International Review of Nuclear Reactor Pressure Vessel Surveillance Programs, West Conshohocken, PA, pp. 432\u2013456. ASTM International (2018)","DOI":"10.1520\/STP160320170009"},{"key":"9_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnucmat.2022.153886","volume":"568","author":"M Kirk","year":"2022","unstructured":"Kirk, M., Hashimoto, Y., Nomoto, A.: Application of a machine learning approach based on nearest neighbors to extract embrittlement trends from RPV surveillance data. J. Nucl. Mater. 568, 153886 (2022)","journal-title":"J. Nucl. Mater."},{"key":"9_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.nucengdes.2023.112236","volume":"406","author":"M Kolluri","year":"2023","unstructured":"Kolluri, M., Martin, O., Naziris, F., et al.: Structural materias research on parameters influencing the material properties of RPV steels for safe long-term operation of PWR NPPs. Nucl. Eng. Des. 406, 112236 (2023)","journal-title":"Nucl. Eng. Des."},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/s41524-022-00760-4","volume":"8","author":"YC Liu","year":"2022","unstructured":"Liu, Y.C., Wu, H., Mayeshiba, T., et al.: Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels. NPJ Comput. Mater. 8(1), 85 (2022)","journal-title":"NPJ Comput. Mater."},{"issue":"11","key":"9_CR9","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.crme.2019.11.009","volume":"347","author":"FJ Mont\u00e1ns","year":"2019","unstructured":"Mont\u00e1ns, F.J., Chinesta, F., G\u00f3mez-Bombarelli, R., et al.: Data-driven modeling and learning in science and engineering. Comptes Rendus M\u00e9canique 347(11), 845\u2013855 (2019)","journal-title":"Comptes Rendus M\u00e9canique"},{"issue":"10","key":"9_CR10","doi-asserted-by":"publisher","DOI":"10.1115\/1.4001056","volume":"132","author":"N Soneda","year":"2010","unstructured":"Soneda, N., Nomoto, A.: Characteristics of the new embrittlement correlation method for the Japanese reactor pressure vessel steels. J. Eng. Gas Turbines Power 132(10), 102918 (2010)","journal-title":"J. Eng. Gas Turbines Power"},{"key":"9_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.nucengdes.2021.111196","volume":"379","author":"H Wang","year":"2021","unstructured":"Wang, H., Villanueva, W., Chen, Y., et al.: Thermo-mechanical behavior of an ablated reactor pressure vessel wall in a nordic BWR under in-vessel core melt retention. Nucl. Eng. Des. 379, 111196 (2021)","journal-title":"Nucl. Eng. Des."},{"issue":"8","key":"9_CR12","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1016\/j.net.2021.02.015","volume":"53","author":"C Xu","year":"2021","unstructured":"Xu, C., Liu, X., Wang, H., et al.: A study of predicting irradiation-induced transition temperature shift for RPV steels with xgboost modeling. Nucl. Eng. Technol. 53(8), 2610\u20132615 (2021)","journal-title":"Nucl. Eng. Technol."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7019-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:14:54Z","timestamp":1699575294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7019-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970186","9789819970193"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7019-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"422","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"95","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}