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The residual deformation of a goaf is studied to improve the foundation stability assessment for metro lines passing through the subsidence area of steeply inclined extra-thick coal seams. The variable mining influence propagation angle is introduced to describe the special form of the rock movement. Based on the modified parameters in the traditional probability integral model, a subsidence prediction model is established. Then, based on the idea of an equivalent mining thickness, Kelvin model is introduced to analyze the creep characteristics of the old goaf, and the dynamic prediction function of the residual subsidence is constructed to realize the dynamic analysis of the residual deformation. Moreover, a case study is used to evaluate the predictive effectiveness of the prediction model, and the results are compared with the monitoring data and numerical simulation results. The results show that the values with a relative error between the predicted value and measured value are in the range of ±7%, indicating that the prediction model based on the mining influence propagation angle is feasible. Thus, the residual deformation prediction model based on the mining influence propagation angle is considered to be suitable for predicting the subsidence of engineering projects crossing a goaf.

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A method for classification of the tunnel wall rock is established based on the rough set theory and unascertained measurement theory. The saturated uniaxial compressive strength, rock mass integrity index, structural surface condition, seepage measurement of groundwater, and the angle between the hole axis and main structural surface are selected as the evaluation indexes. The problem of weight coefficients for these evaluation indexes is converted into that of significance estimating on the attributes in the rough set theory. The proposed method is verified by the wall rock data of Yuanyanghui tunnel. The results show that the proposed method has an excellent performance in good agreement with the practical situation of wall rock and is feasible to classify the wall rock. Finally, the proposed method is applied to Xihualing tunnel of Zhu-Yong Expressway. The results are basically the same as those from Delphi-ideal point method, set pair analysis method, and the actual situation, which proves that the rough set theory and unascertained measurement theory are effective for classification of the wall rock.

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We assessed the association between 5 well-defined polymorphisms of the transforming growth factor-β1 (TGFB1) gene and coronary artery disease (CAD) among patients with hypertension from northeast China. All study participants were classified into patients with CAD (n = 679) and controls (n = 686) according to angiographic results. Genotyping was carried out with the ligase detection reaction method. In single-locus analysis, only genotypes of rs1800469 differed significantly between patients with CAD and controls (P = .001); patients carrying the mutant allele of rs1800469 exhibited a 73% increased risk of CAD (P < .001). Haplotype analysis indicated that haplotype A-T-T-C-C (alleles in the order of rs1800468, rs1800469, rs1800470, rs1800471, and rs1800472) was associated with a 1.49-fold increased risk (P = .003). Interaction analysis identified an overall best 3-locus model including rs1800469, rs1800468, and rs1800471 (P = .003). Taken together, we identified a synergistic interaction between TGFB1 gene multiple polymorphisms that entailed greater risk of CAD in Chinese patients.