Quantitative assessment whole-rock iron content in magnetite protolith based on terahertz time-domain spectroscopy: publisher's note.

This publisher's note serves to correct errors in Appl. Opt.63, 2528 (2024)APOPAI0003-693510.1364/AO.517400.

Do political connections facilitate or inhibit firms' digital transformation? Evidence from China's A-share private listed companies.

Given the advent of the digital era, digital transformation has become necessary for enterprise development. Political connections are the most important resources for enterprise development in most countries. However, the impact of political connections on corporate digital transformation has yet to be verified. This study uses ERNIE, a large language model, to construct a measurement of corporate digital transformation from the perspective of digital technology application through a textual analysis of the annual reports of A-share privately listed companies from 2008 to 2020 and analyzes the impact of political connections on corporate digital transformation and its mechanism of action. The findings demonstrate that political connections have a significant inhibitory effect on corporate digital transformation. This conclusion still holds after a series of robustness and endogeneity tests. The mechanism analyses demonstrate that political connections primarily affect corporate digital transformation through three mechanisms: weakening risk, inhibiting innovation, and enhancing resource crowding. We theoretically expand the understanding of the economic impact of political connections and provide new ideas for accelerating enterprise digital transformation from the perspective of policy makers.

Quantitative assessment whole-rock iron content in magnetite protolith based on terahertz time-domain spectroscopy.

Terahertz time-domain spectroscopy was first used to establish a correlation with the whole-rock iron (TFe) content in different depths of the Bayan Obo protolith. Compared with element content obtained by the traditional method of X-ray fluorescence spectroscopy (XRF), a similar tendency of the absorption coefficient and refractive index is presented. Furthermore, three machine learning algorithms, namely, partial least squares regression (PLSR), random forest (RF), and multi-layer perceptron (MLP), were used to develop a quantitative analytical model for TFe content of the protolith minerals. Among the three algorithms, MLP has the highest detection accuracy, with a model coefficient of determination R 2 reaching up to 0.945. These findings demonstrate that terahertz time-domain spectroscopy can be used to rapidly quantify the TFe elemental content of protolith, providing a method of detecting the content of mineral components.

A Risk Assessment Tool for Identifying Osteoporosis in Older Women with Type 2 Diabetes Mellitus.

PURPOSE: To develop a simple and clinically useful assessment tool for osteoporosis in older women with type 2 diabetes mellitus (T2DM). METHODS: A total of 601 women over 60 years of age with T2DM were enrolled in this study. The levels of serum sex hormones and bone metabolism markers were compared between the osteoporosis and non-osteoporosis groups. The least absolute shrinkage and selection operator regularization (LASSO) model was applied to generate a risk assessment tool. The risk score formula was evaluated using receiver operating characteristic analysis and the relationship between the risk score and the bone mineral density (BMD) and T-value were investigated. RESULTS: Serum sex hormone-binding globulin (SHBG), cross-linked C-telopeptide of type 1 collagen (CTX), and osteocalcin (OC) were significantly higher in the osteoporosis group. After adjustment for age and body mass index (BMI), SHBG was found to be correlated with the T-value or BMD. Then, a risk score was specifically generated with age, BMI, SHBG, and CTX using the LASSO model. The risk score was significantly negatively correlated with the T-value and BMD of the lumbar spine, femoral neck, and total hip (all P<0.05). CONCLUSION: A risk score using age, BMI, SHBG, and CTX performs well for identifying osteoporosis in older women with T2DM.