Experimental Study on Non-Darcian Flow in Phyllite Bimrocks With the Orientation of Blocks

Phyllite bimrocks are widely distributed in the eastern margin of the Tibetan Plateau, and it is the main geomaterial for landslides, slopes, dam basement and subgrades in this area. However, the flow behavior of phyllite bimrocks is unknown, especially the flow behavior of phyllite bimrocks with the orientation of blocks. This paper reports the coupling characteristics of flow and orientation of blocks in phyllite bimrocks. The flow behavior of phyllite bimrocks with different block percentages and block sizes was studied by a series of permeability experiments. A large-scale permeability apparatus was designed, and specimens with varying percentages of block and block sizes were produced by the same dip angle of blocks and compaction degree. Based on the Reynolds number analysis, it was found that the flow in phyllite bimrocks becomes laminar to turbulent under lower hydraulic gradient, and the flow behavior of phyllite bimrocks does not obey Darcy’s law. Furthermore, the Forchheimer equation is better at analyzing the flow behavior of phyllite bimrocks compared with Izbash equation. In addition, based on the coefficients a in the Forchheimer equation, the hydraulic conductivity of phyllite bimrocks can be calculated. The calculation result shows that when the percentage of blocks is 25%, the hydraulic conductivity reaches the minimum. Besides, the hydraulic conductivity increases approximately linear with the block size increase. On the basis of previous studies, coefficients A and B of the Forchheimer equation are detected by the normalized objective function analysis. The results would provide a valuable reference for risk assessment and prevention of phyllite bimrock slope

Key predisposing factors and susceptibility assessment of landslides along the Yunnan–Tibet traffic corridor, Tibetan plateau: Comparison with the LR, RF, NB, and MLP techniques

The Yunnan–Tibet traffic corridor runs through the Three Rivers Region, southeastern Tibetan Plateau, which is characterized by high-relief topography and active tectonics, with favourable conditions for landslides. It is of great significance to identify the key predisposing factors of landslides and to reveal the landslide susceptibility in this area. A total of 2,308 landslides were identified as learning samples through remote sensing interpretation and detailed field surveys, and four machine learning algorithms involving logistic regression (LR), random forest (RF), naïve Bayes (NB) and multilayer perceptron (MLP) were compared to model the landslide susceptibility. Through the multicollinearity test, 13 influential factors were selected as conditioning factors. The area under the curve (AUC) values of LR, RF, NB and MLP models are .788, .918, .785 and .836 respectively, indicating that the four models have good or very good prediction accuracy in landslide susceptibility assessment along the Yunnan–Tibet traffic corridor. In addition, the elevation, slope, rainfall, distance to rivers, and aspect play a major role in landslide development in the study area. The susceptibility zoning map based on the best RF model shows that the areas with high susceptibility and very high susceptibility account for 12.24% and 6.72%, respectively, and are mainly distributed along the Jinsha River, the Lancang River and the G214 highway

Geochemical Mass Balance and Elemental Transport during the Weathering of the Black Shale of Shuijingtuo Formation in Northeast Chongqing, China

An understanding of the processes that control the behavior of major elements with respect to weathering profile is essential to calculate the mobility, redistribution, and mass fluxes of elements. Hence, this study aims to determine the geochemical mass balance, strain, elemental correlation, and transport in weathering profiles. We constructed three weathering profiles for the black shale of Shujingtuo formation. As per the principal component analysis of major elements, density, and pH values, the first component represents the “elemental factor” and the second denotes the “external factor.” The “depletion” pattern is a mass transportation pattern, and Na, K, and Mg are depleted along transect relative to the composition of fresh rock. Fe is redeposited at the bottom half of the saprock zone, whereas Al is accumulated at the regolith zone. The Fe and Al patterns are attributed to the “depletion–addition” and “addition” patterns, respectively. The strain in profiles A and B demonstrates the expansion at the regolith zone and part of the saprock zone. In profile C, however, these zones collapsed at all depths. In chemical weathering, Na, K, Ca, Mg, and Si are depleted in the following order: valley (C) > near mountaintop (B) > ridge (A)

EEG-based major depressive disorder recognition by neural oscillation and asymmetry

BackgroundMajor Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.MethodsIn this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.ResultsRegression analysis found significant predictors of MDD to be beta2 (16–24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24–40 Hz) power in the Medial Occipital Cortex (MOC) (B = −12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12–16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8–12 Hz, B = 9.004, p = 0.013) and theta (4–8 Hz, B = −13.582, p = 0.008) with eyes closed were also significant predictors.ConclusionThe study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.LimitationsThe research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings

Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population.Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model.Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer–Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set.Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients

GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient (k)) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones

GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient (k)) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones

Comparing the prediction performance of logistic model tree with different ensemble techniques in susceptibility assessments of different landslide types

Susceptibility based on different landslide types has rarely been assessed. Therefore, this paper aims to compare the prediction performance of hybrid approaches by combining the logistic model tree (LMT) with Decorate, random subspace, and rotation forest ensemble techniques (De-LMT, RS-LMT, and RoF-LMT) for susceptibility assessments of different landslide types. The Yuzi River catchment along the eastern margin of the Tibetan Plateau was selected as the study area. In this catchment, 478 rockfalls and 167 landslides were identified, and further partitioned into training (70%) and validation (30%) datasets, respectively. Subsequently, 13 conditioning factors were initially considered, and then 11 and 8, respectively, were selected as the final rockfall and landslide influencing parameters to conduct susceptibility models through multicollinearity analysis and information gain. Finally, the model performances were evaluated by area under the receiver operating characteristic curves (AUC). The obtained results demonstrated that elevation and rainfall were most influential factors for rockfall and landslide. For the rockfall prediction, the De-LMT model achieved the best prediction accuracy with the highest AUC (0.939), followed by the RS-LMT (0.938), RoF-LMT (0.928), and LMT (0.919) models. For the landslide prediction, the De-LMT model (AUC = 0.931) outperformed and outclassed the RoF-LMT (0.926), RS-LMT (0.922), and LMT (0.915) models. Therefore, it is reasoned out that all models exhibited satisfactory performance (AUC > 0.9), and the De-LMT model was superior in rockfall and landslide spatial prediction

Characteristics of the In Situ Stress Field and Engineering Effect along the Lijiang to Shangri-La Railway on the Southeastern Tibetan Plateau, China

This work aims to characterize the in situ stress field along the Lijiang to Shangri-La railway and identify possible engineering geological problems when constructing tunnels along this railway on the margin of the Tibetan Plateau. The in situ stress measured at 76 points in 12 boreholes by the hydraulic fracturing method was analysed. A rose diagram of the maximum principal stress direction was plotted based on the measured in situ stress data. The results show that the measured in situ stress is mainly horizontal stress, corresponding to a strike-slip fault-related tectonic stress field with a moderate to high in situ stress value. The main stress values have a good linear relationship with the burial depth, and the maximum horizontal principal stress (σH) increases by 1.1–8.8 MPa per 100 m, with an average gradient value of 3.6 MPa per 100 m. The maximum and minimum horizontal principal stresses and the stress differences increase with depth, and the lateral pressure coefficient (σH/σv) is generally 1–1.5. The ratio of the maximum and minimum effective stresses is less than the threshold at which faulting occurs, resulting in faults that are relatively stable at present. The direction of the maximum horizontal principal stress is oriented at a small angle to the axial direction of the deeply buried tunnel along the railway line; therefore, the tunnel sidewalls could readily deform during the construction process. Rock bursts are expected to occur in strong rock masses with high risk grades, whereas slight to moderate deformation of the rock surrounding the tunnel is expected to occur in weak rock masses. This study has significance for understanding the regional fault activity and issues related to the construction of deeply buried tunnels along the Lijiang to Shangri-La railway

Degradation Characteristics and Mechanism of Black Sandy Dolomite with Fluid Added in a Mechanical Test

Sandy dolomite, being a soluble rock, is prone to dissolution and erosion caused by groundwater, leading to the formation of underground caves and fractures. This may result in geological disasters such as ground subsidence and collapse. In this paper, the changes and mechanical properties of black sandy dolomite after hydrochemistry are studied. A semi-immersion test with different concentrations of iron sulfate solution was carried out to simulate the water-rock interaction in different water environments. After that, scanning electron microscope (SEM) results could reflect the dissolution and pore development of rock by the effect of water-rock interaction from the microscopic. Water-rock interaction enlarges cracks in rocks and dissolves pyrite, carbonate minerals, and other components, reducing the cementation between particles. The change in the mechanical properties of black sandy dolomite under water-rock chemical interaction was revealed by uniaxial compression test. The mechanical properties of the samples exhibit varying degrees of deterioration, with strain increased ranging from 4.96 to 29.58%. The brittleness index modified (BIM) values for each sample ranged from 5.20 to 6.20%, all of which are larger than 4.70% in the natural state