Dataset Information

Reconstructing Chromatic-Dispersion Relations and Predicting Refractive Indices Using Text Mining and Machine Learning.

ABSTRACT: Predicting the properties of materials prior to their synthesis is of great significance in materials science. Optical materials exhibit a large number of interesting properties that make them useful in a wide range of applications, including optical glasses, optical fibers, and laser optics. In all of these applications, refraction and its chromatic dispersion can directly reflect the characteristics of the transmitted light and determine the practical utility of the material. We demonstrate the feasibility of reconstructing chromatic-dispersion relations of well-known optical materials by aggregating data over a large number of independent sources, which are contained within a material database of experimentally determined refractive indices and wavelength values. We also employ this database to develop a machine-learning platform that can predict refractive indices of compounds without needing to know the structure or other properties of a material of interest. We present a web-based application that enables users to build their customized machine-learning models; this will help the scientific community to conduct further research into the discovery of optical materials.

SUBMITTER: Zhao J

PROVIDER: S-EPMC9198980 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Reconstructing Chromatic-Dispersion Relations and Predicting Refractive Indices Using Text Mining and Machine Learning.

Zhao Jiuyang J Cole Jacqueline M JM

Journal of chemical information and modeling 20220519 11

Predicting the properties of materials prior to their synthesis is of great significance in materials science. Optical materials exhibit a large number of interesting properties that make them useful in a wide range of applications, including optical glasses, optical fibers, and laser optics. In all of these applications, refraction and its chromatic dispersion can directly reflect the characteristics of the transmitted light and determine the practical utility of the material. We demonstrate th ...[more]

PMID: 35587269

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Project description:Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention.The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media.A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors.A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC.SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life.

Project description:ObjectivesThe study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining.MethodsThe data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts.ResultsUp to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years.ConclusionsThe study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future.

Dataset Information

Reconstructing Chromatic-Dispersion Relations and Predicting Refractive Indices Using Text Mining and Machine Learning.

Publications

Reconstructing Chromatic-Dispersion Relations and Predicting Refractive Indices Using Text Mining and Machine Learning.

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