{"title":"Aggregate Shocks and the Formation of Preferences and Beliefs","authors":"Paola Giuliano, Antonio Spilimbergo","doi":"10.2139/ssrn.4883869","DOIUrl":"https://doi.org/10.2139/ssrn.4883869","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Micro-jetting and Transverse Waves in Oblique Detonations","authors":"Suryanarayan Ramachandran, Suo Yang","doi":"10.2139/ssrn.4723570","DOIUrl":"https://doi.org/10.2139/ssrn.4723570","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141701636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) from different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and it is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission on Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R2 metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.
在许多医疗和辐射领域，有效剂量的计算对于确保安全和遵守法规限制至关重要。传统上，此类计算使用的是使用详细人体计算模型的蒙特卡洛代码。蒙特卡洛剂量计算非常耗时，而且在构建计算模型和剂量计算时需要不同流程的专业知识。本研究采用各种机器学习（ML）算法来预测不同拟人模型的器官剂量和有效剂量转换系数（DCC）。从蒙特卡洛研究中汇编了一个包含中子能量箱、器官标签、质量和密度的综合数据集，用于训练和评估有监督的 ML 模型。这项研究包括多种模型，其中包括国际辐射防护委员会（ICRP-110、ICRP-116 模型）、可见-人体项目（VIP-man 模型）和医学内部辐射剂量委员会（MIRD-模型）的模型，行数据使用数值数据和器官分类标签数据准备。采用极梯度提升（XGB）、梯度提升（GB）和基于随机森林的 Extra Trees 回归器，使用均方误差、平均绝对误差和 R2 指标，对照已公布的 ICRP 中子 DCC 值评估 ML 模型的性能。结果表明，ML 预测值在较低能量范围内变化较大，而在较高的中子能量范围内变化较小，同时在中等能量范围内与 ICRP 值显示出良好的一致性。此外，分类数据模型与参考剂量密切吻合，表明 ML 在预测基于区域人群的定制模型（如基于沙特体素的模型）的有效剂量方面具有潜力。这项研究为使用 ML 进行高效剂量预测铺平了道路，特别是在需要快速得出结果而又不需要大量计算资源或专业知识的情况下。研究结果还表明，在数据表示和纳入更大的数据集以提高模型准确性和防止过度拟合方面存在潜在的改进空间。因此，ML 方法可以作为个性化剂量测定持续发展的宝贵技术。
{"title":"Machine Learning for Predicting Neutron Effective Dose","authors":"A. Alghamdi","doi":"10.2139/ssrn.4804225","DOIUrl":"https://doi.org/10.2139/ssrn.4804225","url":null,"abstract":"The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) from different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and it is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission on Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R2 metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Energetical Effects of the Edges and Vertices of Face-centered-cubic Pd and Au Nanoparticles: a Density Functional Theory Study","authors":"Akio Ishii","doi":"10.2139/ssrn.4790706","DOIUrl":"https://doi.org/10.2139/ssrn.4790706","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Alternative Measures of Teachers' Value Added and Impact on Short and Long-Term Outcomes: Evidence from Random Assignment","authors":"Victor Lavy, Rigissa Megalokonomou","doi":"10.2139/ssrn.4889207","DOIUrl":"https://doi.org/10.2139/ssrn.4889207","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Gas Trap: Outcompeting Coal vs. Renewables","authors":"Bård Harstad, K. Holtsmark","doi":"10.3386/w32718","DOIUrl":"https://doi.org/10.3386/w32718","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carlo Altavilla, Refet S. Gürkaynak, R. Quaedvlieg
{"title":"Macro and Micro of External Finance Premium and Monetary Policy Transmission","authors":"Carlo Altavilla, Refet S. Gürkaynak, R. Quaedvlieg","doi":"10.2139/ssrn.4809337","DOIUrl":"https://doi.org/10.2139/ssrn.4809337","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Intergenerational Equity and Infinite-Population Ethics: a Survey","authors":"Marcus Pivato, Marc Fleurbaey","doi":"10.2139/ssrn.4751424","DOIUrl":"https://doi.org/10.2139/ssrn.4751424","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finance Research and the UN Sustainable Development Goals – an analysis and forward look","authors":"Yang Su, Brian M. Lucey, Ashish Jha","doi":"10.2139/ssrn.4696722","DOIUrl":"https://doi.org/10.2139/ssrn.4696722","url":null,"abstract":"","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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