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Data provides a foundation for machine learning, which has accelerated data-driven materials design. The scientific literature contains a large amount of high-quality, reliable data, and automatically extracting data from the literature continues to be a challenge. We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys. Within 3 h, 2531 records with both composition and property are extracted from 14,425 articles, covering γ′ solvus temperature, density, solidus, and liquidus temperatures. A data-driven model for γ′ solvus temperature is built to predict unexplored Co-based superalloys with high γ′ solvus temperatures within a relative error of 0.81%. We test the predictions via synthesis and characterization of three alloys. A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.

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Background: Anxiety and depression are common mental health problems among patients with cancer. While many psychological variables have been proven to influence anxiety and depressive symptoms, the variables are not mutually exclusive and their integrated effects on patients with oral cancer are yet unknown. The present study aims to explore the prevalence of anxiety and depressive symptoms among patients with oral cancer, to find out key potentially predictive factors associated with anxiety and depressive symptoms. Method: A cross-sectional study was carried out for Chinese patients with oral cancer between May 2016 and October 2017 in two Grade-A Tertiary Hospitals in Shenyang, China. Two hundred thirty patients with oral cancer were interviewed with questionnaires on demographic variables, Zung Self-Rating Anxiety Scale (SAS), Center for Epidemiologic Studies Depression Scale (CES-D), Herth Hope Index (HHI), Social Impact Scale, Multidimensional Scale of Perceived Social Support (MSPSS), Revised Life Orientation Test (LOT-R), Perceived Stress Scale-10 (PSS-10), and General Perceived Self-efficacy Scale(GSE). Chi-square test, nonparametric test, t-test and logistic regression analyses were conducted where appropriate to explore predictive factors of anxiety symptoms and depressive symptoms. Results: The prevalence of anxiety symptoms and depressive symptoms in the sample population was 36.96% (85/230) and 65.21% (150/230), respectively. Social isolation dimension of stigma (β = 0.436, OR = 1.547, CI:1.211~1.975), optimism (β = − 0.276, OR = 0.759, CI:0.624~0.922), and perceived stress (β = 0.217, OR = 1.243, CI:1.092~1.414) were predictors of anxiety symptoms. Marriage (β = 1.648, OR = 5.198, CI:1.427~18.924), positive readiness and expectancy dimension of hope (β = − 0.505, OR = 0.604, CI:0.395~0.923), social isolation dimension of stigma (β = 0.314, OR = 1.368, CI:1.054~1.776) and perceived stress (β = 0.273, OR = 1.314, CI:1.134~1.524) were predictors of depressive symptoms among oral cancer patients. Conclusion: The prevalence of anxiety symptoms and depressive symptoms was high among oral cancer patients in China. The communal predictors of anxiety and depressive symptoms in patients with oral cancer were levels of perceived stress and social isolation of stigma. In addition, optimism was a predictor of anxiety symptoms and hope was a predictor of depressive symptoms.

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Using a sample of 192 Standard & Poor's 5OOfirms, we examine the impact of corporate governance and ownership context on a particular instance of board entrenchment: adoption of classified boardprovisions. Interestingly, while increased outsider representation measured simply as a proportion of outsiders does not affect the rate of adoption, reliance on a more fine grained measure, which includes other afSiations of outsiders, results in a marginally significant influence. Moreover, it appears that reliance on a simple proportional measure is likely to distort the effects of other variables. Finally, institutional stock ownership is found to be the most significant factor associated with decreased rate of adoption.

The CeO 2 /Ag 3 PO 4 composite photocatalysts are synthesized by an in situ precipitation method. The XRD, FT-IR, XPS, TEM, EDS, and DRS are used to characterize the structure of the samples. The photocatalytic performance of the prepared samples is evaluated by the photocatalytic degradation of methylene blue (MB), rhodamine B (RhB), and ciprofloxacin (CIP). The results show that CeO 2 /Ag 3 PO 4 hybrid materials exhibit much higher photocatalytic activity than the Ag 3 PO 4 alone. The optimal CeO 2 content in CeO 2 /Ag 3 PO 4 composites is found to be molar ratio 1 wt%. Photocurrent response of CeO 2 /Ag 3 PO 4 (1 wt%) is about 1.5 times as high as that of the pure Ag 3 PO 4 . The increase of photocatalytic activity of CeO 2 /Ag 3 PO 4 composites could be mainly attributed to the heterojunction between CeO 2 and Ag 3 PO 4 . The trapping experiment has demonstrated that holes serve as the main active species for the degradation of MB under visible light irradiation. A photocatalytic mechanism is also proposed.

Objectives: This study firstly aimed to explore predicting cognitive impairment at an early stage using a large population-based longitudinal survey of elderly Chinese people. The second aim was to identify reversible factors which may help slow the rate of decline in cognitive function over 3 years in the community.Methods: We included 12,280 elderly people from four waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), followed from 2002 to 2014. The Chinese version of the Mini-Mental State Examination (MMSE) was used to examine cognitive function. Six machine learning algorithms (including a neural network model) and an ensemble method were trained on data split 2/3 for training and 1/3 testing. Parameters were explored in training data using 3-fold cross-validation and models were evaluated in test data. The model performance was measured by area-under-curve (AUC), sensitivity, and specificity. In addition, due to its better interpretability, logistic regression (LR) was used to assess the association of life behavior and its change with cognitive impairment after 3 years.Results: Support vector machine and multi-layer perceptron were found to be the best performing algorithms with AUC of 0.8267 and 0.8256, respectively. Fusing the results of all six single models further improves the AUC to 0.8269. Playing more Mahjong or cards (OR = 0.49,95% CI: 0.38–0.64), doing more garden works (OR = 0.54,95% CI: 0.43–0.68), watching TV or listening to the radio more (OR = 0.67,95% CI: 0.59–0.77) were associated with decreased risk of cognitive impairment after 3 years.Conclusions: Machine learning algorithms especially the SVM, and the ensemble model can be leveraged to identify the elderly at risk of cognitive impairment. Doing more leisure activities, doing more gardening work, and engaging in more activities combined were associated with decreased risk of cognitive impairment.