{"_buckets": {"deposit": "26edf459-d740-401e-9fd3-bf0190e2019d"}, "_deposit": {"id": "2001059", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "2001059"}, "status": "published"}, "_oai": {"id": "oai:soar-ir.repo.nii.ac.jp:02001059", "sets": ["1310"]}, "author_link": [], "control_number": "2001059", "item_1628147817048": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_ab4af688f83e57aa", "subitem_version_type": "AM"}]}, "item_6_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2022-09-02", "bibliographicIssueDateType": "Issued"}, "bibliographicPageStart": "105733", "bibliographicVolumeNumber": "190", "bibliographic_titles": [{"bibliographic_title": "The Journal of Supercritical Fluids"}]}]}, "item_6_description_20": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "The estimation of the solubility of organic compounds in high-temperature water is important for designing chemical processes. This study aimed at predicting the solubility of organic compounds in high-temperature water in the range of 100–250 °C using machine learning. The chemical structure of the organic compound was converted into 196 descriptors (parameters) using an open-source toolkit. The experimental solubility data were regressed using the descriptors, temperature, and water density. The regression methods of ordinary least squares, least absolute shrinkage and selection operator (Lasso), and support vector regression (SVR) were compared. A regression method combining the Lasso and SVR (Lasso + SVR) was developed. The model thus obtained this method was found to accurately predict the solubility of organic compounds in high-temperature water, with a root-mean-square error of 0.5. The findings in this study would be useful for predicting the solubility of any organic compound in high-temperature water.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_6_description_30": {"attribute_name": "資源タイプ（コンテンツの種類）", "attribute_value_mlt": [{"subitem_description": "Article", "subitem_description_type": "Other"}]}, "item_6_description_5": {"attribute_name": "引用", "attribute_value_mlt": [{"subitem_description": "The Journal of Supercritical Fluids.190:105733(2022)", "subitem_description_type": "Other"}]}, "item_6_link_3": {"attribute_name": "信州大学研究者総覧へのリンク", "attribute_value_mlt": [{"subitem_link_text": "長田, 光正", "subitem_link_url": "https://soar-rd.shinshu-u.ac.jp/profile/ja.WmLUZFTp.html"}, {"subitem_link_text": "嶋田, 五百里", "subitem_link_url": "https://soar-rd.shinshu-u.ac.jp/profile/ja.gFcVjFkV.html"}]}, "item_6_publisher_4": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Elsevier B.V."}]}, "item_6_relation_48": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "10.1016/j.supflu.2022.105733"}], "subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1016/j.supflu.2022.105733", "subitem_relation_type_select": "DOI"}}]}, "item_6_select_64": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_6_source_id_35": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1872-8162", "subitem_source_identifier_type": "EISSN"}]}, "item_6_source_id_39": {"attribute_name": "NII ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1872-8162", "subitem_source_identifier_type": "EISSN"}]}, "item_6_source_id_40": {"attribute_name": "書誌レコードID", "attribute_value_mlt": [{"subitem_source_identifier": "AA10678459", "subitem_source_identifier_type": "NCID"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Osada, Mitsumasa", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Tamura, Kotaro", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shimada, Iori", "creatorNameLang": "en"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2024-09-02"}], "download_preview_message": "Download / Preview is available from 2024/9/1.", "file_order": 0, "filename": "JSCF-190-105733.pdf", "format": "application/pdf", "future_date_message": "Download is available from 2024/9/1.", "is_thumbnail": false, "licensetype": "license_5", "mimetype": "application/pdf", "size": 0, "url": {"url": "https://soar-ir.repo.nii.ac.jp/record/2001059/files/JSCF-190-105733.pdf"}, "version_id": "c9236eb7-d59c-4bd5-b067-1eb1c3acc364"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "hydrothermal processes", "subitem_subject_scheme": "Other"}, {"subitem_subject": "subcritical water", "subitem_subject_scheme": "Other"}, {"subitem_subject": "solubility prediction", "subitem_subject_scheme": "Other"}, {"subitem_subject": "machine learning", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Prediction of the solubility of organic compounds in high-temperature water using machine learning", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Prediction of the solubility of organic compounds in high-temperature water using machine learning", "subitem_title_language": "en"}]}, "item_type_id": "6", "owner": "1", "path": ["1310"], "permalink_uri": "http://hdl.handle.net/10091/0002001059", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2022-09-09"}, "publish_date": "2022-09-09", "publish_status": "0", "recid": "2001059", "relation": {}, "relation_version_is_last": true, "title": ["Prediction of the solubility of organic compounds in high-temperature water using machine learning"], "weko_shared_id": -1}