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During underground mining excavation, dynamic disturbances (e.g., blasting) may accelerate rock creeping and induce unstable failure. It is crucial to properly quantify the impact of dynamic disturbances on creeping behavior to understand the long-term effects of underground excavation on rock media. A damage-based creep model was established in this study and implemented in Fast Lagrangian Analysis of Continua in 3D (FLAC3D) to quantify the damage and unstable failure of creeping rock triggered by dynamic disturbance. This model reveals the mechanical behavior of rock under creep-impact loading. It was verified by calculating the viscoelastic deformation around circular excavation, by reproducing the damage process under static loading, and by simulating the rock damage and failure under creep-impact loading. The model was then used to examine the mining-induced creep deformation around an operational shaft in Xincheng Gold Mine, China, where both the mining-induced creep of the country rock mass and dynamic disturbance of blast-induced vibration were considered. Numerical simulation indicated that the dynamic disturbance could accelerate the damage of rock under creep stress, which may be an important factor to trigger the accelerating creep of rock, even though the unstable failure of rock lagged behind the dynamic disturbance. The case study on the shaft stability analysis indicated that the dynamic disturbance could accelerate the time-dependent deformation of the shaft, the first dynamic disturbance induced much more shaft deformation, while in long-term creep (e.g., 5 years), the effect of blasting vibration was not significant. The trend and magnitude of shaft deformation agreed with the in situ measurements, which indicates that the numerical simulation could be used to predict the long-term shaft deformation in future. [ABSTRACT FROM AUTHOR]

The stability graph is an empirical approach to predicting stope performance based on the geometric dimensions of stopes and the geotechnical conditions of the surrounding rock mass. Stability graph is commonly used to design and estimate the stability of open-stope mines and underground openings. Most engineers calculate the rock stress factor by the stability graph method considering the maximum principal stress at the center of the critical face and the uniaxial compressive strength of the intact rock, whereas the spatial multidimensional stress state, compressive strength of the rock mass, and tensile strength of the rock mass are not taken into account. In this study, the maximum and the minimum principal stresses dominating the rock stress factor are introduced into the stability graph. The rock stress factor (A) is modified by the Mohr-Coulomb criterion with tensile cutoff. The modified stability graph method was validated by adopting Cavity Monitoring System (CMS) laser technology, and it was then successfully applied in a case study at the Alhada Lead-Zinc Mine in Inner Mongolia. Compared with Mitri's method, the proposed stability graph expands the scope of the narrow vein stope design dimensions. The modified rock stress factor in the stability graph provides a technical basis for the subsequent recovery of residual orebodies. [ABSTRACT FROM AUTHOR]

Mining of ore body in the vicinity of a shaft has a significant influence on its stability. The in situ monitoring and numerical simulation are employed to analyze the effect of shaft pillar extraction at Xincheng Gold Mine. The XI# ore body is recently found around and beneath the shaft, and mining in this area may be detrimental to the shaft. Firstly, on the base of geological survey and in situ displacement monitoring, mechanical parameters of rock mass are obtained and the displacement around the shaft is measured. Secondly, the sensitivities of five main factors that may affect the shaft displacement are analyzed by means of orthogonal experiment according to the numerical simulation with FLAC3D. Finally, a numerical model is established according to the in situ condition; in order to forecast the shaft displacement induced by mining activities of XI# orebody, the Mining Priority Index (MPI) is put forward and used to select the optimal mining sequence. Based on the comparison between the numerical results with the monitoring data, it is determined that the ore within 100 m from the shaft is not suggested to be extracted until the last period of the shaft life. [ABSTRACT FROM AUTHOR]

In order to smooth ore production during the transition from open pit to underground mining at Yanqianshan Iron Mine, China, it is necessary to select an appropriate mining method to operate simultaneously open-pit mining and underground mining for inner-slope (hanging-wall) ore-body. Based on practical geologic setting of the hanging-wall ore-body, a comprehensive evaluation model for selecting the underground mining method to extract handing-wall ore-body was proposed, which is constituted by 3 sub-systems of safety factors, production factors and technical-economic factors. This evaluation model could be analyzed by the mutation progression method, which based on catastrophe theory and fuzzy mathematics. In addition, the FLAC is employed to simulate the mining process of hanging-wall ore-body, and finally the optimal mining scheme is determined. By using mutation progression analysis and numerical modeling, some useful conclusions for hanging-wall ore-body mining have been drawn, which will be beneficial to mining safety during transitional period from open-pit to underground mining. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is a common disease in women. Early detection and early treatment can reduce breast cancer mortality. Studies have shown that breast cancer microcalcifications is one of the important clinical manifestations of early breast cancer, and sometimes even the only manifestation. When the mammography image shows typical malignant microcalcification, it can be diagnosed as breast cancer without any other signs of malignancy. In the aided diagnosis of microcalcifications, it is a crucial step to automatically find and locate regions of interest containing microcalcifications. However, the existing feature extraction method for microcalcifications only extracts features in the time domain or wavelet domain, and does not completely represent all the information of the region of interest. An extraction method based on the combination of Dual-Tree Complex Wavelet Transform (DTCWT) and texture features is proposed in the paper., Methods: First, the processing operations including denoising, enhancement, and edge detection were performed on mammograms. Sub-image segmentation is then performed. DTCWT features and texture features are extracted for each sub-image.DTCWT features are combined with texture features, and then genetic algorithm is used for feature optimization. The features are classified by the Extreme Learning Machine (ELM) to achieve rapid detection and automatic extraction of ROI with microcalcifications. The experimental results verify that the feature model proposed in this paper has the highest detection rate for ROI regions. The ROI region extracted by the proposed feature model was used as subsequent experimental data. Three different methods were used to detect the microcalcifications, including Top-hat, wavelet transform, and methods combining Top-Hat and wavelet transform., Results: The method was applied to 100 mammograms from the mammograms database of women in Northeast China. In the automatic extraction of ROI, the accuracy, sensitivity, specificity, positive accuracy and negative accuracy of the proposed model combined with DTCWT were 95.92%, 96.71%, 92.20%, 93.65%, 96.33%, respectively. When the Top-hat algorithm was used for microcalcifications detection, the sensitivity reached 89.6%, and the false positive detection rate was 2.6. When the wavelet transform algorithm was used for microcalcifications detection, the sensitivity was 91.1%, and the false positive detection rate was 3.28. When the combined algorithm was used for microcalcifications detection, the sensitivity was 86.7%, and the false positive detection rate decreased to 1.35., Conclusions: The proposed model combined with DTCWT features achieves better result in the automatic extraction of ROI. Moreover, in the subsequent detection of microcalcifications based on three methods, the three methods achieved better results in sensitivity and false positive detection rate, respectively.