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Traditional method of determining closure and initiation stress of fractured rocks by analyzing the stress-strain curve has problems such as strong subjectivity and large errors. This study utilized the rock closure stress values and onset stress values determined by three traditional methods, namely, axial strain method, fracture volume method and empirical value taking method, as the base database. The Student t distribution theory was used to obtain a confidence interval based on its overall distribution of values and to achieve a combination of the advantages of multiple methods. Within confidence interval, the Monte Carlo stochastic simulation was used to determine the convergence interval of the second stage to further improve the accuracy. Finally, mean value of the randomly sampled values after reaching the convergence stage was taken as the probability value of rock closure and crack initiation stress. The results showed that the 3 traditional methods for calculating rock closure and initiation stresses are significantly different. In contrast, the proposed method biases more towards multi-numerical distribution intervals and also considers the preference effects of different calculation methods. In addition, this method does not show any extreme values that deviate from the confidence intervals, and it has strong accuracy and stability compared to other methods. [ABSTRACT FROM AUTHOR]

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper's method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment. [ABSTRACT FROM AUTHOR]

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a new debris flow risk assessment method based on the Monte Carlo Simulation and an Improved Cloud Model. The new method tests the consistency of coupling weights according to the characteristics of the Cloud Model firstly, so as to determine the weight boundary of each evaluation index. Considering the uncertain characteristics of weights, the Monte Carlo Simulation is used to converge the weights in a minimal fuzzy interval, then the final weight value of each evaluation index is obtained. Finally, a hierarchical comprehensive cloud is established by the Improving Cloud Model, which is used to input the comprehensive expectation composed of weights to obtain the risk level of debris flow. Through statistical analysis, this paper selects Debris flow scale (X1), Basin area (X2), Drainage density (X3), Basin relative relief (X4), Main channel length (X5), Maximum rainfall (X6) as evaluation indexes. A total of 20 debris flow gullies were selected as study cases (8 debris flow gullies as model test, 12 debris flow gullies in reservoir area as example study). The comparison of the final evaluation results with those of other methods shows that the method proposed in this paper is a more reliable evaluation method for debris flow prevention and control. [ABSTRACT FROM AUTHOR]

This study was designed to investigate whether Resveratrol (Res) could be a prophylactic factor in the prevention of I/R injury and to shed light on its underlying mechanism. Primary culture of neonatal rat cardiomyocytes were randomly distributed into three groups: the normal group (cultured cardiomyocytes were in normal conditions), the I/R group (cultured cardiomyocytes were subjected to 2 h simulated ischemia followed by 4 h reperfusion), and the Res+I/R group (100 mmol/L Res was administered before cardiomyocytes were subjected to 2 h simulated ischemia followed by 4 h reperfusion). To test the extent of cardiomyocyte injury, several indices were detected including cell viability, LDH activity, Na+-K+-ATPase and Ca2+-ATPase activity. To test apoptotic cell death, caspase-3 activity and the expression of Bcl-2/Bax were detected. To explore the underlying mechanism, several inhibitors, intracellular calcium, SOD activity and MDA content were used to identify some key molecules involved. Res increased cell viability, Na+-K+-ATPase and Ca2+-ATPase activity, Bcl-2 expression, and SOD level. While LDH activity, capase-3 activity, Bax expression, intracellular calcium and MDA content were decreased by Res. And the effect of Res was blocked completely by either L-NAME (an eNOS inhibitor) or MB (a cGMP inhibitor), and partly by either DS (a PKC inhibitor) or Glybenclamide (a KATP inhibitor). Our results suggest that Res attenuates I/R injury in cardiomyocytes by preventing cell apoptosis, decreasing LDH release and increasing ATPase activity. NO, cGMP, PKC and KATP may play an important role in the protective role of Res. Moreover, Res enhances the capacity of anti-oxygen free radical and alleviates intracellular calcium overload in cardiomyocytes. [ABSTRACT FROM AUTHOR]

Background: The brain biochemical changes of social anxiety have not been clarified although there have been a limited number of MR spectroscopic studies which utilized metabolite/creatine ratios. Present study aimed to explore the alteration of absolute metabolite concentration in social anxiety disorder using quantitative MR spectroscopy. Materials and Methods: With a 3.0T MR scanner, single voxel MR spectroscopy (stimulated echo acquisition mode, TR/TE/ TM= 2000/20/16 ms) was performed in the left dorsolateral prefrontal cortex and related regions of nine medication-free patients with social anxiety disorder and nine controls. Absolute metabolite concentration was calculated using tissue water as the internal reference and corrected for the partial volume of cerebrospinal fluid. Results: In the left dorsolateral prefrontal cortex, the N-acetyl aspartate/creatine ratio of patients was significantly higher than that of controls, and this was due to the decrease of creatine concentration instead of the increase of N-acetyl aspartate concentration. Furthermore, the creatine concentration of the left dorsolateral prefrontal cortex was negatively correlated with the scores of Liebowitz social anxiety scale. Conclusions: The alteration of creatine level in the left dorsolateral prefrontal cortex suggests abnormal energy metabolism and correlates with symptom severity in social anxiety disorder. And metabolite concentration is preferable to metabolite/ creatine ratio for the investigation of individual, absolute metabolite changes in this region of social anxiety disorder. [ABSTRACT FROM AUTHOR]