Experimental Study on Shear Behavior and Acoustic Emission Characteristics of Nonpersistent Joints

The shear behavior of rock discontinuities controls the stability of rock masses to a great extent. In this paper, laboratory shear tests were performed on rock-like materials with different cracks to study the effect of nonpersistent joints on the shear behavior of rock masses. The results show that the variation trends of the shear stress-displacement curves of specimens with different cracks are generally similar and have the same stage characteristics. When the crack length is relatively short, the elastic stage is prolonged, the peak shear strength decreases, and the shear displacement corresponding to the peak shear strength and the residual shear strength increases with the increase of the crack length. When the crack length is relatively long, the elastic stage is shortened, the peak shear strength decreases, and the shear displacement corresponding to the peak shear strength increases with the increase of the crack length. The peak shear stress gradually decreases with the increase of the crack length. The shear strength of the specimens with unilateral cracks is much higher than that of the specimens with bilateral cracks. The shear strength of the specimens is affected not only by the crack length but also by the crack distribution. The acoustic emission (AE) count peak occurs when the shear stress drops sharply and has an inverse "S"-type variation trend with the increase of the crack length. The inclination angle of the fracture decreases, the roughness of the fracture surface decreases, and the proportion of the wear area on the fracture surface increases gradually with the increase of the crack length. The AE source decreases with the increase of the crack length, and their locations are obviously asymmetric. This work can greatly contribute to the insight into the shear failure mechanism of rock discontinuities with nonpersistent joints

Experimental Investigation on the Law of Grout Diffusion in Fractured Porous Rock Mass and Its Application

Because of the limitation of mining techniques and economic conditions, large amounts of residual coal resources have been left in underground coal mines around the world. Currently, with mining technology gradually developing, residual coal can possibly be remined. However, when residual coal is remined, caving areas might form, which can seriously affect the safety of coal mining. Hence, grouting technology is put forward as one of the most effective technologies to solve this problem. To study the grouting diffusion in fractured rock mass, this paper developed a visualization platform of grouting diffusion and a three-dimensional grouting experimental system that can monitor the grout diffusion range, diffusion time and grout pressure; then, a grouting experiment is conducted based on this system. After that, the pattern of the grouting pressure variation, grout flow and grout diffusion surface are analyzed. The relationship among some factors, such as the grouting diffusion radius, compressive strength of the grouted gravel, porosity, water-cement ratio, grouting pressure, grouting time, permeability coefficient and level of grout, is quantitatively analyzed by using MATLAB. The study results show that the flow pattern of the grout in fractured porous rock mass has a parabolic shape from the grouting hole to the bottom. The lower the level is, the larger the diffusion range of the grout is. The grouting pressure has the greatest influence on the grouting diffusion radius, followed by the grouting horizon and water-cement ratio. The grouting permeability coefficient has the least influence on the grouting diffusion radius. The grout water-cement ratio has the greatest influence on the strength of the grouted gravel, followed by the grouting permeability. The grouting pressure coefficient has the least amount of influence on the grouting diffusion radius. According to the results, the grouting parameters are designed, and a layered progressive grouting method is proposed. Finally, borehole observation and a core mechanical property test are conducted to verify the application effect. This grouting technology can contribute to the redevelopment and efficient utilization of wasted underground coal resources

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Objective. To establish a prediction model for the risk evaluation of chronic kidney disease (CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients with CKD and 1948 patients without CKD admitted to the Tongde Hospital of the Zhejiang Province from January 1, 2008, to December 31, 2018, were retrospectively analyzed. Spearman’s correlation was used to analyze the relationship between CKD and laboratory parameters. XGBoost, random forest, Naive Bayes, support vector machine, and multivariate logistic regression algorithms were employed to establish prediction models for the risk evaluation of CKD. The accuracy, precision, recall, F1 score, and area under the receiver operating curve (AUC) of each model were compared. The new bidirectional encoder representations from transformers with light gradient boosting machine (MD-BERT-LGBM) model was used to process the unstructured data and transform it into researchable unstructured vectors, and the AUC was compared before and after processing. Results. Differences in laboratory parameters between CKD and non-CKD patients were observed. The neutrophil ratio and white blood cell count were significantly associated with the occurrence of CKD. The XGBoost model demonstrated the best prediction effect (accuracy = 0.9088, precision = 0.9175, recall = 0.8244, F1 score = 0.8868, AUC = 0.8244), followed by the random forest model (accuracy = 0.9020, precision = 0.9318, recall = 0.7905, F1 score = 0.581, AUC = 0.9519). Comparatively, the predictions of the Naive Bayes and support vector machine models were inferior to those of the logistic regression model. The AUC of all models was improved to some extent after processing using the new MD-BERT-LGBM model. Conclusion. The new MD-BERT-LGBM model with the inclusion of unstructured data has contributed to the higher accuracy, sensitivity, and specificity of the prediction models. Clinical features such as age, gender, urinary white blood cells, urinary red blood cells, thrombin time, serum creatinine, and total cholesterol were associated with CKD incidence

Construction of the expression vector of the HPV58 E7.

<p>(A) Construction of pGEX-4T2-(HPV58-E7) vector. The lane M1 shows the TaKaRa DL2000 DNA marker, lane 1 shows the HPV58 E7 gene (PCR from human HPV58-positive cervical epithelial cells); lane 2 is the pGEX-4T2 vector; lane 3 is the cleaved pGEX-4T2 vector by <i>Eco</i>R I and <i>Bam</i>H I; lane 4 shows the reconstructed pGEX-4T2-(HPV58-E7) vector; lane 5 is the verification of pGEX-4T2-(HPV58-E7) vector which was cleaved by <i>Eco</i>R I and <i>Bam</i>H I; the lane M2 is TaKaRa DL 15,000 DNA marker. (B) Construction of pEGFP-C1-(HPV58-E7) vector. The lane M1 shows the TaKaRa DL2000 DNA marker, lane 1 shows the HPV58 E7 gene (PCR from pGEX-4T2-(HPV-58E7) vector); lane 2 is the pEGFP-C1 vector; lane 3 is the cleaved pEGFP-C1 vector by <i>Eco</i>R I and <i>Bam</i>H I; lane 4 shows the reconstructed pEGFP-C1-(HPV58-E7) vector; lane 5 is the verification of pEGFP-C1-(HPV58-E7) vector which was cleaved by <i>Eco</i>R I and <i>Bam</i>H I; the lane M2 is TaKaRa DL 15,000 DNA marker.</p

Examination of the cross-reaction of HPV 58 E7 antibody.

<p>(A) western blotting for the detection of HPV58 E7. Lanes 1, 2 and 3 are the pure protein of HPV 58, 16 and 18 E7. (B) Immunofluorescent analysis for the detection of HPV58 E7 in SiHa and HeLa cells. (C) Immunohistochemistry stain for the detection of HPV58 E7 in cervical cancer cells, which was HPV 16- or 18-positive, HPV 58 was negative. 1 is the HPV16-positive sample and 2 is the HPV18-positive sample.</p

Survival curves of patients participating/not participating in the endoscopic screening program.

<p>Survival curves of patients participating/not participating in the endoscopic screening program.</p