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Background: The psychometric properties of the simplified Chinese version of the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) have not been assessed. Therefore, we aimed to assess its validity, reliability, and responsiveness.Patients and Methods: A Chinese version of the PRO-CTCAE and the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (QLQ-C30) were distributed to 1580 patients from four cancer hospitals in China. Validity assessments included construct validity, measured by Pearson's correlations and confirmatory factor analysis (CFA), and known-groups validity, measured by t-tests. The assessment of reliability included internal consistency, measured by Cronbach's ɑ, and test-retest reliability, measured by the intraclass correlation (ICC). Responsiveness was assessed by standardized response means (SRMs).Results: Data from 1555 patients who completed the instruments were analyzed. The correlations were high between PRO-CTCAE items and parallel QLQ-C30 symptom scales (r > 0.60, p < 0.001), except for fatigue (severity: r = 0.49). Moreover, CFA showed the PRO-CTCAE structure was a good fit with the data (Root Mean Square Error of Approximation = 0.046). Known-groups validity was also confirmed. Cronbach's ɑ of all item clusters were greater than 0.9 and the median test-retest reliability coefficients of the 38 items were 0.85 (range = 0.71-0.91). In addition, the SRMs of PRO-CTCAE items were greater than 0.8, indicating strong responsiveness.Conclusion: The simplified Chinese version of the PRO-CTCAE showed good reliability, validity, and responsiveness. [ABSTRACT FROM AUTHOR]

The first-order grey model is appropriate for monotonic time series, while the grey Verhulst model is appropriate for periodic time series. The grey Verhulst model is widely used for predicting practical nonlinear time series that contain fluctuations. Despite the successful application of the grey models in practice, researchers show that grey models still have room for improvement. Moreover, recent research shows that grey models are sensitive to initial conditions. In many practical problems, the value of initial conditions is not accurate and suffers from noise or approximation errors, resulting in inaccurate prediction. To address these difficulties, an improved grey Verhulst method using particle swarm optimization is proposed in this paper. The proposed model consists of two stages. In the first stage, a second-order polynomial with unknown coefficients is used to approximate the 1- AGO. In the second stage, the initial element of the time series is modified to improve the accuracy of the prediction. The particle swarm optimization method is used to search for the unknown coefficients. It is found that PSO is a suitable optimization method for the proposed method because it is stable and generates a sequence of points that converges to a globally optimal solution. To demonstrate the efficiency and stability of the proposed method, it was applied to five practical problems, namely, gas production in China, the number of hazardous chemical accidents, CO2 emissions in Russia, the number of domestic tourists in China, and traffic flows in Canada. Numerical results show that the method is more accurate and robust than existing methods. • The proposed method needs only four elements for predicting the future elements of time series. • The paper proposes a second order polynomial for approximating 1-AGO. • The method makes more accurate prediction than existed methods. • The proposed method is applied on various practical forecasting problems successfully. [ABSTRACT FROM AUTHOR]

Large deformations and displacements of rock blocks may occur under high stresses around deep underground rock engineering works such as deep mines. The original discontinuous deformation analysis (DDA) developed by Shi and Goodman was dedicated to analyze large deformations, rotations and displacements of rock blocks by accumulating small components of these quantities in a time-marching scheme. The small rotation angle approximation adopted in the original DDA may induce block expansion with rotation (rotation error). Based on solid mechanics, the free expansion is caused by (1) the error due to the approximation of the real behavior using a displacement function, and (2) the theory used to describe the geometrical relationship between movement (displacement and rotation) and deformation. The original DDA uses the geometrical relationship that is based on small strain theory. Small strain theory assumes a linear relationship between displacement and strain that does not consider the high order components and the decomposition of rotation and displacement. The finite deformation theory, however, decomposes the displacement and rotation and gives a nonlinear relationship between displacement and strain. Therefore, the finite deformation theory can solve the block rotation problem at a higher accuracy. The DDA method is extended by coupling with the finite deformation theory. The implementation of this method is first verified by three illustration examples, and later employed to modelling a work face excavation of a coal mine in China. The stress field and displacement field are investigated. The results show that the occurrence of the first fracture of the key stratum increases the displacement and the stress. The second fracture of the key stratum partially releases the stress. The periodic weighting phenomenon is also reflected in this modelling. [ABSTRACT FROM AUTHOR]